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Technische Universitat Wien

DISSERTATION

System Capacity Optimizationof UMTS FDD Networks

ausgefuhrt zum Zwecke der Erlangung des akademischen Grades eines Doktorsder technischen Wissenschaften

eingereicht an der Technischen Universitat WienFakultat fur Elektrotechnik und Informationstechnik

von

Alexander GERDENITSCHMatrikelnummer 9656381

A-7022 Loipersbach, Berggasse 1geboren in Eisenstadt, Osterreich am 21. Oktober 1976

Wien, im Juni 2004

First Supervisor: o. Univ.-Prof. i.R. Dr. Ernst BONEKInstitut fur Nachrichtentechnik und Hochfrequenztechnik

Fakultat fur Elektrotechnik und InformationstechnikTechnische Universitat Wien, Austria

Second Supervisor: a.o. Univ.-Prof. Dr. Gunther RAIDLInstitut fur Computergraphik und Algorithmen

Fakultat fur InformatikTechnische Universitat Wien, Austria

Abstract

In this thesis I investigate the problem of capacity optimization in UMTS FDDnetworks. The goal is to improve the capacity of the network, measured as ser-ved users, only by changing the base station parameters. The focus is on theoptimization of antenna tilt and common pilot channel (CPICH) power of thebase stations. These parameter adjustments improve the UMTS radio networkcapacity by means of reducing inter-cell interference, achieve cell load sharing,and optimize base station power resources.

Altogether five different algorithms for finding the best settings of antenna tiltand CPICH power are presented. The first three optimization algorithms, RuleBased Approach, Simulated Annealing and Adaptive Rule Based Approach, arelocal techniques. Furthermore, a global technique, the Genetic Algorithm, will bepresented. Also, an Analytic Optimization Algorithm will be discussed.

The fitness function used for the algorithms considers the number of served usersas the main optimization goal. For the Genetic Algorithm I use a fitness functionthat additionally also considers coverage and soft handover.

First, the Rule Based Approach is adressed. The optimization process is charac-terized by reducing the CPICH power and increasing the antenna downtilt in theindividual cells according to a configurable rule set. Subsequently, this algorithmis extended by incorporating Simulated Annealing. Here, the decision whether totake a worse result is, in contrast to the first method, independent of the ruleset. The third local algorithm is also a further development of the Rule BasedApproach. The main difference between the Adaptive Rule Based Approach andthe other two local approaches is that CPICH power and antenna tilt are changedtogether, and that also an increase of CPICH power and antenna up tilting ispossible during the optimization process.

Further, a Genetic Algorithm is introduced which I improved by taking operatorsthat are adapted for the UMTS capacity optimization problem by taking intoaccount the quality of the network. In addition, a local optimization is includedto improve the performance.

Finally, I address an Analytical Optimization Algorithm. Beside antenna tilt andCPICH power settings, this algorithm optimizes also the antenna azimuth.

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The performance of the algorithms is evaluated using a static UMTS FDD net-work simulator on two virtual scenarios of a typical European city. In the firstscenario the network covers the whole area of the city. The second scenario onlyspans across downtown.

With the different algorithms, I show improvements in capacity of up to 105%compared to the initial settings. The Genetic Algorithm performs best, but withthe drawback of a high computation time. If we compare the three local optimiza-tion techniques, Rule Based Approach, Simulated Annealing and Adaptive RuleBased Approach, we see that the Adaptive Rule Based Approach achieves thehighest improvement. The computation effort for all three algorithms is appro-ximately the same. The Analytic Optimization Algorithm shows, with only fivenetwork evaluations, almost the same optimization result as the local algorithms.

Zusammenfassung

Diese Dissertation beschaftigt sich mit der Kapazitatsoptimierung in UMTS Mo-bilfunknetzen. Das Ziel ist, die Kapazitat im Netz, gemessen an der Anzahlder bedienten Teilnehmer, nur durch Optimierung der Basisstationsparameterzu erhohen. Zur Optimierung werden die Antennenneigung sowie die Sendelei-stung des common pilot channel (CPICH) herangezogen. Eine korrekte Einstel-lung dieser Parameter bewirkt eine Kapazitatssteigerung des UMTS Mobilfunk-netzes durch Reduzierung der Interferenz der Nachbarzellen. Weiters kommt eszu einer gleichmassigen Aufteilung der Last auf die einzelnen Zellen und zur Op-timierung der Leistungsressourcen der Basisstationen.

In dieser Arbeit werden insgesamt funf verschiedene Algorithmen zur Suche nachder optimalen Einstellung der Antennenneigung sowie der CPICH-Leistung vor-gestellt. Die ersten drei Optimierungsalgorithmen, ein auf einem Regelwerk ba-sierter Algorithmus, ein auf diesem basierender adaptiver Algorithmus und einSimulated Annealing Algorithmus, sind lokale Techniken. Weiters wird auch eineglobale Technik, ein genetischer Algorithmus, untersucht. Der letzte diskutierteAlgorithmus ist ein analytischer Optimierungsalgorithmus.

Die verwendete Fitnessfunktion beschreibt das Optimierungsziel (Kapazitatssteig-erung) durch die Anzahl der bedienten Mobilfunkteilnehmer. Fur den genetischenAlgorithmus wird die Fitnessfunktion um den Grad der Netzabdeckung und dieAnzahl der Teilnehmer, die mit mehr als einer Basisstation gleichzeitig verbundensind, erweitert.

Zu Beginn stelle ich in meiner Arbeit den auf einem Regelwerk basierten Algorith-mus vor. Der Optimierungsprozess bei diesem Algorithmus zeichnet sich durcheine Erhohung der Antennenneigung und Reduzierung der CPICH-Leistung inden einzelnen Zellen mittels eines konfigurierbaren Regelwerkes aus. In weite-rer Folge wird der Algorithmus durch das Einbinden von Simulated Annealingerweitert. Die Entscheidung, ob ein schlechtes Ergebnis akzeptiert wird, ist imGegensatz zum vorigen Algorithmus unabhangig vom Regelwerk. Der dritte Algo-rithmus ist ebenfalls eine Erweiterung des ersten Algorithmus. Der grundlegendeUnterschied zu den vorigen Algorithmen ist, dass nun die Antennenneigung unddie CPICH-Leistung gemeinsam adaptiert werden, und dass eine Erhohung der

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CPICH-Leistung sowie eine Reduzierung der Antennenneigung wahrend des Op-timierungsprozesses ebenfalls zulassig ist.

Weiters behandle ich einen genetischen Algorithmus, der an meine Problemstel-lung angepasst ist. Mittels angepasster Operatoren wird auch die Qualitat desMobilfunknetzes zur Optimierung herangezogen. Ein Bestandteil des genetischenAlgorithmus ist auch eine lokale Optimierung, mit dem Ziel die Leistung desAlgorithmus weiter zu steigern.

Schließlich behandle ich in meiner Dissertation einen analytischen Optimierungs-algorithmus. Dieser Algoritmus optimiert neben der Antennenneigung und derCPICH-Leistung auch den Azimutwinkel der Antenne.

Die Leistungsfahigkeit der einzelnen Algorithmen wird mit Hilfe eines statischenUMTS FDD Netzwerksimulators auf zwei virtuellen Szenarien einer typischen eu-ropaischen Großstadt bewertet. Das erste Szenario umfasst das komplette Stadt-gebiet, wogegen das zweite nur das Zentrum der Stadt abdeckt.

Mit den einzelnen Algorithmen zeige ich auf beiden Szenarien eine Kapazitatssteig-erung von bis zu 105% verglichen zur anfanglichen Parametereinstellung. Dergenetische Algorithmus liefert das beste Ergebnis, jedoch mit dem Nachteil derlangen Laufzeit. Unter den lokalen Optimierungsverfahren schneidet der adap-tive Regelwerk basierte Algorithmus am besten ab. Die Laufzeit ist jedoch furalle drei Algorithmen ungefahr gleich. Der analytische Optimierungsalgorithmuszeigt eine ahnliche Kapazitatssteigerung wie die lokalen Verfahren, jedoch mitdem Vorteil, dass dieser Algorithmus nur funf statt mehr als hundert Iterationenbenotigt.

Acknowledgment

I am deeply grateful to Prof. Ernst Bonek for his guidance and invaluable supportduring my work. I thank him for his encouragement during the course of thiswork and for various suggestions improving the quality of this thesis.

I am also very grateful to Martin Toeltsch and Thomas Neubauer of SYMENA,Software & Consulting GmbH, for the numerous discussions and their fruitfulcollaboration. Further I thank them for providing the static UMTS FDD networksimulator CAPESSOTM .

Special thanks go to Thomas Baumgartner and Werner Weichselberger for manycritical and useful suggestions and discussions.

My very great appreciation goes to all my colleagues, Plamen Dintchev, KlausKopsa, Christian Pommer, Huseyin Ozcelik, Elmar Trojer, Markus Herdin, Ger-hard Gritsch and Biljana Badic for their fruitful collaboration.

Very special thanks to Stefan Jakl, who shared the office with me and workedtogether with me on the same project, for the numerous discussions and theexcellent office atmosphere. Further I thank him for proof-reading this thesis. Itwas a pleasure to share the office with you.

Thanks are also included to Wolfgang Karner and Yee Yang Chong, who madetheir master thesis in our project under the guidance of Stefan and me.

I owe also thanks to Michael Feher of Dipl.-Ing. Dr. Hermann Buhler GmbH,Prof. Gunther Raidl of the Algorithms and Data Structures Group from theVienna University of Technology and Hendrik Rogier of the ElectromagneticsGroup from the Department of Information Technology (INTEC), Ghent Uni-versity, Belgium.

Last, but not least, I would like to thank my parents and family for the tremen-dous support during my studies. Without their assistance I wouldn’t have reachedmy goals.

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Contents

1 Introduction 1

1.1 Background and Standardization of W-CDMA . . . . . . . . . . . 2

1.2 Objectives of the UMTS System . . . . . . . . . . . . . . . . . . . 4

1.3 Motivation for UMTS Optimization . . . . . . . . . . . . . . . . . 6

1.4 Structure of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . 7

2 Radio System Aspects 9

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.2 UTRA FDD System Description . . . . . . . . . . . . . . . . . . . 10

2.2.1 Channel and Frame Structure . . . . . . . . . . . . . . . . 10

2.2.2 Modulation, Spreading and Scrambling . . . . . . . . . . . 13

2.3 Transport Channels and Physical Channels . . . . . . . . . . . . . 15

2.3.1 Synchronization Channel . . . . . . . . . . . . . . . . . . . 15

2.3.1.1 Primary SCH . . . . . . . . . . . . . . . . . . . . 15

2.3.1.2 Secondary SCH . . . . . . . . . . . . . . . . . . . 15

2.3.2 Common Pilot Channel . . . . . . . . . . . . . . . . . . . 17

2.3.2.1 Primary Common Pilot Channel . . . . . . . . . 17

2.3.2.2 Secondary Common Pilot Channel . . . . . . . . 18

2.3.3 Broadcast Channel . . . . . . . . . . . . . . . . . . . . . . 18

2.3.4 Random Access Channel . . . . . . . . . . . . . . . . . . . 18

2.3.5 Forward Access Channel . . . . . . . . . . . . . . . . . . . 19

2.3.6 Paging Channel . . . . . . . . . . . . . . . . . . . . . . . . 19

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viii CONTENTS

2.3.7 Dedicated Channel . . . . . . . . . . . . . . . . . . . . . . 20

2.3.8 Downlink Shared Channel . . . . . . . . . . . . . . . . . . 20

2.3.9 Uplink Common Packet Channel . . . . . . . . . . . . . . 20

2.3.10 Necessary Common Channels in a UMTS Network . . . . 21

2.4 Physical Layer Procedures . . . . . . . . . . . . . . . . . . . . . . 21

2.4.1 Cell Search . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.4.2 Power Control . . . . . . . . . . . . . . . . . . . . . . . . . 22

2.4.3 Handover . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

2.5 Services and Applications . . . . . . . . . . . . . . . . . . . . . . 25

2.5.1 UMTS Forum Traffic Classes . . . . . . . . . . . . . . . . 26

2.5.2 3GPP UMTS QoS Classes . . . . . . . . . . . . . . . . . . 30

2.5.3 Traffic Forecast for UMTS Network Optimization . . . . . 31

3 Overview of Optimization Techniques 33

3.1 General Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

3.2 Local Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

3.3 Simulated Annealing . . . . . . . . . . . . . . . . . . . . . . . . . 37

3.4 Tabu Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

3.5 Evolutionary Algorithms . . . . . . . . . . . . . . . . . . . . . . . 40

3.5.1 Genetic Algorithms . . . . . . . . . . . . . . . . . . . . . . 42

3.5.1.1 Selection . . . . . . . . . . . . . . . . . . . . . . 44

3.5.1.2 Recombination . . . . . . . . . . . . . . . . . . . 45

3.5.1.3 Mutation . . . . . . . . . . . . . . . . . . . . . . 45

3.5.1.4 Applications for Genetic Algorithms . . . . . . . 46

3.6 Ant Colony Optimization . . . . . . . . . . . . . . . . . . . . . . . 46

CONTENTS ix

4 Coverage- and Capacity-limiting Factors 49

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

4.2 Uplink and Downlink Coverage-limited Scenarios . . . . . . . . . 50

4.3 Uplink Capacity-limited Scenarios . . . . . . . . . . . . . . . . . . 53

4.3.1 Insufficient Uplink Power . . . . . . . . . . . . . . . . . . . 53

4.3.2 Uplink Cell Load Limitation . . . . . . . . . . . . . . . . . 54

4.4 Downlink Capacity-limited Scenarios . . . . . . . . . . . . . . . . 55

4.4.1 Cell Power Limitation . . . . . . . . . . . . . . . . . . . . 55

4.4.2 Orthogonal Variable Spreading Factor (OVSF) Code Lim-itation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

4.4.3 Code Power Limitation . . . . . . . . . . . . . . . . . . . . 57

4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

5 Key Optimization Parameters 61

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

5.2 Antenna Parameters . . . . . . . . . . . . . . . . . . . . . . . . . 62

5.2.1 Antenna Azimuth . . . . . . . . . . . . . . . . . . . . . . . 62

5.2.2 Antenna Tilt . . . . . . . . . . . . . . . . . . . . . . . . . 63

5.3 Common Pilot Channel (CPICH) Power . . . . . . . . . . . . . . 66

5.4 A Short Investigation of CPICH and Antenna Tilt Changing . . . 68

5.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

6 Fitness Function and Performance Indicators 71

6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

6.2 Fitness Function . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

6.2.1 Basic Fitness Function . . . . . . . . . . . . . . . . . . . . 72

6.2.2 Extended Fitness Function . . . . . . . . . . . . . . . . . . 72

6.2.3 Used Fitness Function . . . . . . . . . . . . . . . . . . . . 73

6.3 Grade of Service . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

6.4 Performance Indicators . . . . . . . . . . . . . . . . . . . . . . . . 75

6.4.1 Outaged Mobiles . . . . . . . . . . . . . . . . . . . . . . . 75

6.4.2 Quality Factor . . . . . . . . . . . . . . . . . . . . . . . . . 76

6.4.3 Summary of Performance Indicators . . . . . . . . . . . . . 77

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7 Simulation Environment 79

7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

7.2 The Used UMTS FDD Simulator . . . . . . . . . . . . . . . . . . 79

7.2.1 Parameters of the Network Simulator . . . . . . . . . . . . 81

7.2.2 CPICH Coverage Verification Modes . . . . . . . . . . . . 82

7.2.2.1 Mode 1 . . . . . . . . . . . . . . . . . . . . . . . 82

7.2.2.2 Mode 2 . . . . . . . . . . . . . . . . . . . . . . . 83

7.3 Simulator Interface . . . . . . . . . . . . . . . . . . . . . . . . . . 83

7.4 Network Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . 84

7.4.1 Big Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . 85

7.4.2 Small Scenario . . . . . . . . . . . . . . . . . . . . . . . . 86

8 Optimization Algorithms 89

8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89

8.2 Local Optimization Algorithms . . . . . . . . . . . . . . . . . . . 90

8.2.1 Rule Based Approach . . . . . . . . . . . . . . . . . . . . . 91

8.2.2 Simulated Annealing . . . . . . . . . . . . . . . . . . . . . 92

8.2.3 Adaptive Rule Based Approach . . . . . . . . . . . . . . . 94

8.2.3.1 Why Adjust CPICH Power and Antenna Tilt To-gether? . . . . . . . . . . . . . . . . . . . . . . . 94

8.2.3.2 Algorithm Description . . . . . . . . . . . . . . . 96

8.3 Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 99

8.3.1 Representation . . . . . . . . . . . . . . . . . . . . . . . . 99

8.3.2 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

8.3.2.1 Initial Population . . . . . . . . . . . . . . . . . . 102

8.3.2.2 Selection . . . . . . . . . . . . . . . . . . . . . . 102

8.3.2.3 Recombination . . . . . . . . . . . . . . . . . . . 104

8.3.2.4 Mutation . . . . . . . . . . . . . . . . . . . . . . 106

8.3.2.5 Local Optimization . . . . . . . . . . . . . . . . . 107

CONTENTS xi

8.3.2.6 Adding Users as New Impulse . . . . . . . . . . . 108

8.3.2.7 Reduced Population Size . . . . . . . . . . . . . . 108

8.3.2.8 Implementation for Distributed Computing . . . 109

8.3.3 Summary of Genetic Algorithm . . . . . . . . . . . . . . . 112

8.4 Analytic Optimization Algorithm . . . . . . . . . . . . . . . . . . 112

8.4.1 Azimuth Adjustment Strategy . . . . . . . . . . . . . . . . 113

8.4.1.1 Algorithm Description . . . . . . . . . . . . . . . 115

8.4.2 Strategy for Antenna Downtilt . . . . . . . . . . . . . . . . 117

8.4.3 CPICH Power Level Adjustment Strategy . . . . . . . . . 118

8.4.3.1 Strategy for CPICH Coverage Verification Mode 1 121

8.4.3.2 Strategy for CPICH Coverage Verification Mode 2 122

9 Algorithm Performance Analysis 125

9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

9.2 Results for the Rule Based Approach . . . . . . . . . . . . . . . . 125

9.2.1 Various Investigations with the Rule Based Approach . . . 126

9.2.1.1 Optimization on Different Snapshots . . . . . . . 127

9.2.1.2 Analyzing Optimization Results with Different Snap-shots . . . . . . . . . . . . . . . . . . . . . . . . . 127

9.2.1.3 Optimization with Modified Parameter Ranges . 131

9.2.1.4 Optimization on Adapted Start Scenarios . . . . 133

9.2.2 Runtime of the Rule Based Approach . . . . . . . . . . . . 133

9.3 Results for Simulated Annealing . . . . . . . . . . . . . . . . . . . 134

9.3.1 Results with Slow Cooling . . . . . . . . . . . . . . . . . . 134

9.3.2 Results with Geometric Cooling . . . . . . . . . . . . . . . 137

9.3.3 Results with Improved Rule Set . . . . . . . . . . . . . . . 137

9.3.4 Runtime of Simulated Annealing . . . . . . . . . . . . . . 139

9.4 Results for the Adaptive Rule Based Approach . . . . . . . . . . . 139

9.5 Results with the Genetic Algorithm . . . . . . . . . . . . . . . . . 141

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9.5.1 CPICH Coverage Verification Mode 1 . . . . . . . . . . . . 142

9.5.1.1 Results on the Small Network Scenario . . . . . . 142

9.5.1.2 Results on the Big Network Scenario . . . . . . . 145


9.5.2.1 Results on the Small Network Scenario . . . . . . 148

9.5.2.2 Results on the Big Network Scenario . . . . . . . 148

9.5.3 Runtime of the Genetic Algorithm . . . . . . . . . . . . . 154

9.6 Results for the Analytic Optimization Algorithm . . . . . . . . . . 155



9.6.3 Runtime of the Analytic Optimization Algorithm . . . . . 159

9.7 Comparison of the Various Approaches . . . . . . . . . . . . . . . 159

10 Summary and Conclusion 163

11 Appendix 167

A UMTS - Network Structure 169

B 3GPP COST 259 Channel Models 173

B.1 Model Descriptions . . . . . . . . . . . . . . . . . . . . . . . . . . 173

B.2 3GPP Considerations . . . . . . . . . . . . . . . . . . . . . . . . . 174

C RAKE Reception 177

D Rule Set for Rule Based Approach 181

E Rule Sets for Simulated Annealing 183

F Rule Sets for Adaptive Rule Based Approach 185

G Parameter File for Genetic Algorithm 189

CONTENTS xiii

H Flowcharts for Analytic Optimization Algorithm 191

I Simulation Parameters 197

J Frequently Used Acronyms 201

K Frequently Used Symbols 205

L Curriculum Vitae 209

Bibliography 213

xiv CONTENTS

List of Tables

2.1 Technical overview of FDD mode. . . . . . . . . . . . . . . . . . . 10

2.2 Service Characteristics (source: [101]). . . . . . . . . . . . . . . . 26

2.3 Effective call duration (source: [101]). . . . . . . . . . . . . . . . . 29

2.4 Operational environment and cell types (source: [101]). . . . . . . 31

3.1 Analogy between the thermodynamic simulation of annealing andoptimization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

4.1 Example link budget for a data service (source: [68]). . . . . . . . 51

4.2 Example uplink link budgets for illustrating the impact of servicedata rate (source: [68]). . . . . . . . . . . . . . . . . . . . . . . . 52

4.3 Typical base station transmit power configurations (source: [68]). 56

6.1 Assignment fitness function - optimization algorithm. . . . . . . . 74

6.2 Used performance indicators for the several algorithms. . . . . . . 77

8.1 Example rule set for Rule Based Optimization Algorithm. . . . . 92

8.2 CPICH power and antenna tilt adjustment. . . . . . . . . . . . . 96

8.3 Example of CPICH power and antenna tilt limitation & stepsizesettings. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

8.4 Best setting of optimization parameters for GA. . . . . . . . . . . 113

8.5 Rules for adjusting the antenna downtilt according to the meanelevation angle in CPICH coverage verification mode 1. . . . . . . 118

8.6 Rules for adjusting the antenna downtilt according to the meanelevation angle in CPICH coverage verification mode 2. . . . . . . 119

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xvi LIST OF TABLES

9.1 Results for the Rule Based Approach with CPICH verificationmode 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

9.2 Results for the Rule Based Approach with CPICH verificationmode 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

9.3 Results for the Rule Based Approach with different parameter ranges.131

9.4 Results for the Rule Based Approach with adapted antenna tilt inthe start scenario. . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

9.5 Results for the Rule Based Approach with adapted CPICH powerin the start scenario. . . . . . . . . . . . . . . . . . . . . . . . . . 134

9.6 Results for Simulated Annealing with Slow Cooling, different val-ues for β. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

9.7 Results for Simulated Annealing with Slow Cooling, different val-ues for TC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136

9.8 Results for Simulated Annealing with Geometric Cooling. . . . . . 138

9.9 Results for Simulated Annealing with improved rule set. . . . . . 139

9.10 Results for Adaptive Rule Based Approach with CPICH verifica-tion mode 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

9.11 GA settings for the best optimization run on the small networkscenario with CPICH verification mode 1. . . . . . . . . . . . . . 142

9.12 Evaluation of the best result with 100 different snapshots on thesmall network scenario with CPICH verification mode 1. . . . . . 144

9.13 GA settings for the best optimization run on the big network sce-nario with CPICH verification mode 1. . . . . . . . . . . . . . . . 145

9.14 Evaluation of the best result with 100 different snapshots on thebig network scenario with CPICH verification mode 1. . . . . . . 147

9.15 Evaluation of the best result with 100 different snapshots on thesmall network scenario with CPICH verification mode 2. . . . . . 150

9.16 GA settings for the best optimization run on the small networkscenario with CPICH verification mode 2. . . . . . . . . . . . . . 150

9.17 Evaluation of best result with 100 different snapshots on the bignetwork scenario with CPICH verification mode 2. . . . . . . . . . 153

9.18 GA settings for the best optimization run on the big network sce-nario with CPICH verification mode 2. . . . . . . . . . . . . . . . 153

LIST OF TABLES xvii

9.19 Results for the Analytic Optimization Algorithm with CPICH ver-ification mode 1 (40 snapshots). . . . . . . . . . . . . . . . . . . . 156

9.20 Results for the Analytic Optimization Algorithm with CPICH ver-ification mode 2 (40 snapshots) with CPICHEc/I0 threshold -12 dBand required coverage probability in worst case of 0.5/0.75. . . . . 158

9.21 Results for the Analytic Optimization Algorithm with CPICH ver-ification mode 2 (40 snapshots) with CPICHEc/I0 threshold -12 dBand required coverage probability in worst case of 0.8/0.98. . . . . 158

9.22 Comparison of the different algorithms with 50 different snapshotson the big network scenario with CPICH verification mode 1. . . . 160

B.1 Preliminary environments identified by COST 259. . . . . . . . . 174

B.2 Propagation properties proposed by COST 259 and considered by3GPP. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175

D.1 Standard rule set used for Rule Based Approach. . . . . . . . . . 181

E.1 Rule set 1 for Simulated Annealing. . . . . . . . . . . . . . . . . . 183

E.2 Rule set 2 for Simulated Annealing. . . . . . . . . . . . . . . . . . 184

F.1 Rule set 1 for Adaptive Rule Based Approach. . . . . . . . . . . . 185




I.1 Base station antenna parameters in simulator CAPESSOTM . . . 197

I.2 System parameters in simulator CAPESSOTM . . . . . . . . . . . 198

I.3 Channel parameters in simulator CAPESSOTM . . . . . . . . . . 199

I.4 Mobile station parameters in simulator CAPESSOTM . . . . . . . 199

I.5 User parameters in simulator CAPESSOTM . . . . . . . . . . . . 199

xviii LIST OF TABLES

List of Figures

1.1 Spectrum allocation for Europe, China, Japan, Korea and NorthAmerica [100]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.2 ITU-R IMT-2000 grouping [54]. . . . . . . . . . . . . . . . . . . . 5

1.3 UMTS network environment [48]. . . . . . . . . . . . . . . . . . . 6

2.1 Channel structure for W-CDMA [97]. . . . . . . . . . . . . . . . . 11

2.2 Frame structure for the downlink DPCH. . . . . . . . . . . . . . . 12

2.3 Frame structure for the uplink DPCH. . . . . . . . . . . . . . . . 12

2.4 Transmit path for DPCH in the downlink direction. . . . . . . . . 13

2.5 Transmit path for DPCH in the uplink direction. . . . . . . . . . 13

2.6 Code tree for OVSF (orthogonal variable spreading factor) codes. 14

2.7 Relation between spreading and scrambling. . . . . . . . . . . . . 14

2.8 Transport-channel to physical-channel mapping. . . . . . . . . . . 16

2.9 Synchronization Channel (SCH) structure. . . . . . . . . . . . . . 16

2.10 Common Pilot Channel (CPICH) structure. . . . . . . . . . . . . 17

2.11 Code groups. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

2.12 Outer and inner TPC loop. The green-colored blocks reside in thephysical (PHY) layer and the yellow-colored blocks reside in theradio resource control (RRC) layer (source: [74]). . . . . . . . . . 23

2.13 Softer handover. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.14 Soft handover. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

2.15 Types of services (source: [47]). . . . . . . . . . . . . . . . . . . . 27

2.16 Packet transmission over the UMTS air interface (source: [100]). . 29

xix

xx LIST OF FIGURES

2.17 3GPP traffic classes classification. . . . . . . . . . . . . . . . . . . 30

3.1 Local Search algorithm. . . . . . . . . . . . . . . . . . . . . . . . 36

3.2 Simulated Annealing algorithm. . . . . . . . . . . . . . . . . . . . 39

3.3 Tabu Search algorithm. . . . . . . . . . . . . . . . . . . . . . . . . 40

3.4 The structure of an Evolutionary Algorithm. . . . . . . . . . . . . 41

3.5 Genetic Algorithm. . . . . . . . . . . . . . . . . . . . . . . . . . . 43

3.6 Canonical Genetic Algorithm. . . . . . . . . . . . . . . . . . . . . 43

3.7 1-point crossover. . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

3.8 Mutation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

5.1 Adjustment of base station azimuth. . . . . . . . . . . . . . . . . 62

5.2 Horizontal pattern of base station antenna (in dB). . . . . . . . . 63

5.3 Adjustment of base station downtilt. . . . . . . . . . . . . . . . . 64

5.4 Vertical pattern of base station antenna (in dB). . . . . . . . . . . 64

5.5 Capacity for different CPICH power and antenna tilt settings. (Ca-pacity is measured as served users.) . . . . . . . . . . . . . . . . . 68

6.1 The three tradeoffs of UMTS optimization (source: [75]). . . . . . 73

7.1 Schematic principle of a static Monte Carlo simulator. . . . . . . . 80

7.2 Interface between network simulator and optimization algorithm. . 84

7.3 Base station location and one user distribution of the big networkscenario. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

7.4 Base station location and one user distribution of the small networkscenario. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

8.1 Structure of local optimization process. . . . . . . . . . . . . . . . 90

8.2 Rule Based Optimization Algorithm. . . . . . . . . . . . . . . . . 91

8.3 Extension of Rule Based Optimization algorithm to Simulated An-nealing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

8.4 Why adjust CPICH power and antenna tilt together? . . . . . . . 95

8.5 Detailed flowchart of Adaptive Rule Based Approach. . . . . . . . 98

LIST OF FIGURES xxi

8.6 Representation of individuals for capacity optimization. . . . . . . 99

8.7 Flowchart of Genetic Algorithm approach. . . . . . . . . . . . . . 101

8.8 Recombination operator. . . . . . . . . . . . . . . . . . . . . . . . 104

8.9 Mutation operator. . . . . . . . . . . . . . . . . . . . . . . . . . . 106

8.10 Detailed flowchart of local optimization. . . . . . . . . . . . . . . 107

8.11 Distributed Computing. . . . . . . . . . . . . . . . . . . . . . . . 110

8.12 Distributed Client Scheduler. . . . . . . . . . . . . . . . . . . . . 111

8.13 Client. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

8.14 Best and worst case of antenna directions in a regular hexagonalgrid. Source: [80]. . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

8.15 Worst case and best case of base station azimuth in a regular grid,pathgain in dB. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

8.16 Check, if bs reference(i) looks to bs to interleave. . . . . . . . . 116

8.17 Window 1 contains the most angles. . . . . . . . . . . . . . . . . . 116

8.18 Elevation angle of the cell area. . . . . . . . . . . . . . . . . . . . 117

8.19 Function for adjustment of the antenna downtilt according to themean elevation angle in CPICH coverage verification mode 1. . . . 119

8.20 Function for adjustment of the antenna downtilt according to themean elevation angle in CPICH coverage verification mode 2. . . . 120

8.21 Transmit power of one cell in the optimization area of the bignetwork scenario. . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

9.1 Results for the Rule Based Approach on different snapshots. . . . 127

9.2 Results for the Rule Based Approach on different snapshots afteroptimization for snapshot 1. . . . . . . . . . . . . . . . . . . . . . 128

9.3 Results for the Rule Based Approach on different snapshots afteroptimization for snapshot 11. . . . . . . . . . . . . . . . . . . . . 129


9.5 Results for the Rule Based Approach on different snapshots afteroptimization for snapshot 6. . . . . . . . . . . . . . . . . . . . . . 130


xxii LIST OF FIGURES

9.7 3D matrix of parameter range limits for the Rule Based Approach. 132

9.8 Block diagram for the simulation of the four rule sets over 50 snap-shots. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140

9.9 Comparison of cdf curves for the four rules sets of the AdaptiveRule Based Approach (50 snapshots). . . . . . . . . . . . . . . . . 140

9.10 Results for the Genetic Algorithm on the small network scenariowith CPICH verification mode 1. . . . . . . . . . . . . . . . . . . 143

9.11 Optimization run for the best result on the small network scenariowith CPICH verification mode 1. . . . . . . . . . . . . . . . . . . 144

9.12 Results for the Genetic Algorithm on the big network scenario withCPICH verification mode 1. . . . . . . . . . . . . . . . . . . . . . 146

9.13 Optimization run for the best result on the big network scenariowith CPICH verification mode 1. . . . . . . . . . . . . . . . . . . 147

9.14 Mean results over 100 snapshots for the Genetic Algorithm on thesmall network scenario with CPICH verification mode 2. . . . . . 149

9.15 Optimization run for the best result on the small network scenariowith CPICH verification mode 2. . . . . . . . . . . . . . . . . . . 151

9.16 Mean results over 100 snapshots for the Genetic Algorithm on thebig network scenario with CPICH verification mode 2. . . . . . . 152

9.17 Optimization run for the best result on the big network scenariowith CPICH verification mode 2. . . . . . . . . . . . . . . . . . . 154

9.18 Results for the Analytic Optimization Algorithm with CPICH ver-ification mode 1 (40 snapshots). . . . . . . . . . . . . . . . . . . . 157

9.19 Results for the Analytic Optimization Algorithm with CPICH ver-ification mode 2 (40 snapshots) with CPICHEc/I0 threshold -12 dB.159

9.20 Comparison of the different algorithms on the big network scenariowith CPICH verification mode 1. . . . . . . . . . . . . . . . . . . 161

A.1 Overview and basic entities of the UMTS network structure. . . . 171

B.1 Channel shape (power delay profile) with multiple clusters. Source:[7]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175

B.2 Reduced complexity channel model parameters. Source: [7]. . . . 176

C.1 The principle of maximum ratio combining within the CDMARAKE receiver. Source: [54]. . . . . . . . . . . . . . . . . . . . . 178

LIST OF FIGURES xxiii

C.2 Block diagram of a W-CDMA RAKE receiver. Source: [54]. . . . 179

C.3 Schematic block diagram of matched-filter. . . . . . . . . . . . . . 179

H.1 Automatic azimuth adjustment routine for turning base stationsto critical spots. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192

H.2 Automatic azimuth adjustment routine for interleaving base stations.193

H.3 Automatic tilt adjustment routine. . . . . . . . . . . . . . . . . . 194

H.4 Automatic CPICH adjustment routine for CPICH coverage verifi-cation mode 2, function: set CPICH coverage in total scenario. . . 195

H.5 Automatic CPICH adjustment routine for CPICH coverage ver-ification mode 2, function: set CPICH coverage in optimizationarea. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196

xxiv LIST OF FIGURES

Chapter 1

Introduction

Third generation (3G) mobile communication systems in Europe are known asthe Universal Mobile Telecommunication System (UMTS). UMTS is expected toplay a key role in creating the future mass market for high-quality multimediacommunications that will approach 2 billion users worldwide by the year 2010[102]. Enabling anytime, anywhere connectivity to the Internet is just one ofthe opportunities for UMTS networks. UMTS will bring more than just mobilityto the Internet. The major market opportunity will build on new possibilitiesfor mobile users like multi-media-messaging, location-based services, personalizedinformation, and entertainment experiences. Due to the number of new applica-tions and services a significant redistribution of operators revenue will take placewithin the next years. Nobody knows the killer application per se, but all marketstudies are in complete agreement in one point: packet data will increasinglydominate the traffic flows. By 2007, predictions from the heyday of the UMTSeuphoria state that more data than voice will flow over mobile networks [102, 35].This is an amazing statistic considering that mobile cellular networks today arealmost exclusively voice.

In 2nd generation systems, coverage planning and frequency planning, involvingoptimization, are the most important but also sufficient issues for operating thenetwork. Coverage prediction and capacity estimation are mostly well separa-ble. In UMTS networks, where all users operate on the same frequency carrier,the number of simultaneous connections directly influences the system capac-ity. Multiple services like speech, Internet and high data rate interactive serviceswill co-exist. Since higher bit-rate services will require higher capacity, the basestation density will have to be increased.

In this thesis I focus on the problem of capacity optimization for UMTS FDDnetworks. The goal is to improve the capacity of the network only by changingthe base station parameters and not by using more sites.

1

2 CHAPTER 1. INTRODUCTION

1.1 Background and Standardization of W-CDMA

In June 1987 the RACE I project (Research of Advanced Communication Tech-nologies in Europe) was initiated by the European Union. This was the officialstart of the research and development activities towards a third generation mo-bile communication system in Europe. After RACE I several European R&Dprograms e.g. RACE II and ACTS (Advanced Communications Technologiesand Services) [10] followed in order to support 3rd generation mobile communica-tions system development. Within ACTS, the FRAMES project (Future RadioWideband Multiple Access System) was initiated with the objective of defininga proposal for a UMTS radio access system.

From a global point of view, the work for the development of 3rd generationmobile systems started in 1992, when the WARC (World Administrative RadioConference) of ITU (International Telecommunications Union) identified the fre-quencies around 2GHz for use by future third generation mobile systems. TheITU calls these IMT 2000 1. The frequency bands and geographical areas, wherethese different bands are defined, are shown in Figure 1.1. For IMT 2000 alto-gether 230MHz in two frequency bands, 1885 - 2025MHz and 2110 - 2200MHz,were reserved.

The proposals for the UMTS Terrestrial Radio Access (UTRA) air interface re-ceived by the milestone were grouped into five concept groups in ETSI in June1997, after their submission and presentation during 1996 and early 1997. Thefollowing groups were formed:

• Alpha concept: Wideband CDMA (W-CDMA) [37]

• Beta concept: OFDMA [38]

• Gamma concept: Wideband TDMA (W-TDMA) [39]

• Delta concept: Wideband TDMA/CDMA [40]

• Epsilon concept: ODMA [41]

ETSI decided between the technologies in January 1998 [42], selecting W-CDMAas the standard for the UTRAN air interface on the paired frequency bands,i.e. for FDD (Frequency Division Duplexing) operation, and TDMA/CDMA forthe operation within unpaired spectrum allocation, i.e. for TDD (Time DivisionDuplexing) operation. These combined modes formed the basis for the ETSIproposal to the ITU as a candidate IMT-2000 radio transmission technology. It

1International Mobile Telecommunications 2000. 2000 can be interpreted either as ”for theyear 2000”, or for the frequency band around ”2000MHz”.

1.1. BACKGROUND AND STANDARDIZATION OF W-CDMA 3

Figure 1.1: Spectrum allocation for Europe, China, Japan, Korea and NorthAmerica [100].

took 10 years from the initiation of the European research programs (RACE I+II,ACTS) to reach a decision of the UTRA technology. The detailed standardizationof UTRA proceeded within ETSI until the work was handed over to the 3rd

Generation Partnership Project (3GPP). The technical work was transferred to3GPP with the contribution of UTRA in early 1999.

Meanwhile the standardization bodies of Japan (TTC, ARIB), Korea (TTA),China (CWTS) and USA (T1P1) were independently choosing their own 3G ra-dio access technologies. Even though the original goal of the standardizationprocess was a single common global IMT-2000 radio interface, the goal to achievea single worldwide standard was extremly difficult from the beginning. In Europeand Asia, including Japan and Korea, W-CDMA is to be used utilizing 5MHzblocks in the frequency bands at around 2GHz. In North America that spec-trum had already been auctioned for operators using second generation systemsin blocks of 1.25MHz (see Figure 1.1). Hence, 3rd generation services have tobe implemented not only within the existing frequency bands, but also with adifferent radio technology, since there were no cohesive 5MHz frequency blocksavailable in the US. Since it became evident that it would be very difficult toachieve identical specifications, initiatives were started to create a single forumfor a common UTRA standardization. The 3rd Generation Partnership Project(3GPP - http://www.3gpp.org) was set up in 1998 with this objective, including


TTC/ARIB for Japan, ETSI for Europe, TTA for Korea, T1P1 for the US andCWTS for China as partners. The detailed technical work of this group startedin early 1999, with the aim of having the first version of the common specifica-tion, called Release-99, ready by the end of 1999. Within 3GPP, four differenttechnical specification groups (TSG) were set up as follows:

• Radio Access Network TSG

• Core Network TSG

• Service and System Aspects TSG

• Terminals TSG

Within these groups the one most relevant to the W-CDMA technology is theRadio Access Network TSG (RAN TSG), which produced the Release-99 of theUTRA air interface.

The development of the ITU recommendations for 3rd generation mobile com-munication systems ran like this: In the first phase of the IMT-2000 candidatesubmission process, the ITU received a number of different proposals. In thesecond phase of the process, evaluation results were received from proponent or-ganizations as well as from other evaluation groups. The ITU IMT-2000 processwas finalized at the end of 1999, when the detailed specification was created andthe radio interface specifications were approved by ITU-R [54, 95]. The membersof ITU-R IMT-2000 family are shown in Figure 1.2. The TDMA subgroup consitsof a TDMA single carrier (UWC-136) and a TDMA Multi-Carrier2 (DECT) con-cept. The CDMA interface consists of the Direct-Spread (UTRA FDD) and theMulti-Carrier (cdma2000) part. The TDD part of the CDMA concept consistsof the 3GPP proposal, UTRA TDD (3.84Mchip/s) and the Chinese narrowbandversion TD-SCDMA (1.2288Mchip/s). Good overviews on the 3GPP proposalsfor IMT-2000 are given in [22, 29, 54, 84]

1.2 Objectives of the UMTS System

The UMTS forum (www.umts-forum.org) is an open and independent panel,which was founded by a multiplicity of companies and telecom authorities withthe goal to advance the development and implementation of the UMTS system.The UMTS forum defines the UMTS system in the following way: ”UMTS will bea mobile communication system that can offer significant user benefits including

2Multi-Carrier here means FDMA.

1.2. OBJECTIVES OF THE UMTS SYSTEM 5

Figure 1.2: ITU-R IMT-2000 grouping [54].

high-quality wireless multimedia services to a convergent network of fixed, cellularand satellite components. It will deliver information directly to users and providethem with access to new and innovative services and applications. It will offermobile personalized communications to the mass market regardless of location,network or terminal used.” The idea was to harmonize the present different sys-tems in only one wireless infrastructure, with a multiplicity of possible servicesand applications. It should be possible to implement communication servicesfrom each person to each other person, at all times, on any place, without dis-turbing delay, at low costs and with selectable quality and security. Some of thegoals, which were important during the definition phase of the UMTS system arepresented in the following list:

• To create an integrated system, in which the user can use services of simpleand consistent manner.

• Offer the possibility to choose between services of different networks andoperators.

• To deliver a broad spectrum of mobile telecommunication services. Theseservices should also include data services with flexible data rates from 32kbit/s up to 2 Mbit/s, which could also be used in fixed networks andspecial services for mobile communication. The quality should be the sameas in fixed networks.

• The services should be offered by mobile terminals (mobile phones, ter-minals fixed in cars,...) and by fixed terminals in all environments (flats,offices, means of transportation, public buildings, i.e. from small picocellsin buildings up to big macrocells with coverage by satellite). A possibleenvironment is shown in Figure 1.3.

• Worldwide roaming.


• The cellular structure of the network should accomplish the condition fora complete personal communication network.

• Open architecture, to facilitate an easy introduction of new technologiesand services.

• Support of different types of terminals (e.g. mobile terminals, PDAs, note-books,...)

A small, cheap, light and simple usable terminal should be established at themass market for standard applications. Beside these terminals, there will be aset of products, which are convenient for applications with higher requirements.

Figure 1.3: UMTS network environment [48].

1.3 Motivation for UMTS Optimization

The UMTS radio interface can carry voice and data services with various datarates, traffic requirements and quality of service (QoS) targets. Furthermore, theoperating environments vary considerably from outdoor macro cells to indoormicro cells. Careful configuration of the many network and cell parameters is

1.4. STRUCTURE OF THE THESIS 7

required and crucial to the network operator, because they determine the capa-bility to provide services, influence the QoS, and account for a major portion ofthe total network deployment and maintenance costs.

Optimization is needed, both in the planning stage to optimize the network con-figuration for investment saving as well as after the deployment of the networkto satisfy growing service demand (see [73, 106]). However, there are numer-ous configurable parameters which are multi-dimensional and interdependent,and their influence on the network is highly non-linear. Hence, finding optimumnetwork configuration is a very complex and time-consuming task. Automatedoptimization algorithms are needed to perform the optimization process quicklyand efficiently, with minimal contribution from the operational expenditure.

Different optimization techniques are known, which are suitable for the problemof UMTS base station parameter optimization. All these techniques have sev-eral advantages and also disadvantages. During the work for this thesis severalautomated optimization algorithms (local, global and analytic algorithms) weredeveloped, which are presented and compared in the present work.

Base station parameters such as antenna azimuth, antenna tilt and common pilotchannel power (CPICH) are the three most common optimization parameters thathave significant influence on network capacity. By optimizing these three keyparameters, the network capacity can be boosted without additional investmentneeded.

The work on this thesis led to four papers at international conferences andone submission to an Asian journal. Some of the results obtained during thecourse of this thesis have also been discussed at working group 3 of COST 273(www.lx.it.pt/cost273).

1.4 Structure of the Thesis

The thesis is organized as follows:

• Chapter 2 gives a short overview of the system description of UMTS FDD.Beside the theoretical basics of W-CDMA, transport channels, physicalchannels and the physical layer procedures, also a short introduction onpossible services and applications is presented.

• Chapter 3 gives an overview over common and well known optimizationtechniques. This chapter discusses local as well as global optimization al-gorithms.


• Chapter 4 presents the UMTS radio network coverage and capacity limitingfactors. For the several limiting reasons possible solutions are given.

• In Chapter 5 the parameters, which are used during this thesis for optimiz-ing the capacity in a UMTS FDD network, are described in more detail.Further, the influence of these parameters on the capacity in the networkis explained.

• Chapter 6 introduces the used fitness functions for the optimization as wellas the performance indicators, which are taken into account during theoptimization process.

• Chapter 7 is a description of the simulation environment. The main char-acteristics of the used simulator are specified as well as the simulation sce-narios and the user distribution in the scenarios.

• In Chapter 8 of the thesis all the developed optimization algorithm arepresented. Altogether five different strategies are introduced. First, the op-timization with local algorithms are studied. Further, a global optimizationapproach and an analytic algorithm are presented in this chapter.

• Chapter 9 presents some results achieved with the several algorithms.

• Finally, Chapter 10 summarizes and concludes the thesis.

Chapter 2

Radio System Aspects

2.1 Introduction

UMTS Terrestrial Radio Access (UTRA) denotes the air interface of the UMTSsystem. The basis for UTRA is spread spectrum technology. Very good andelaborate descriptions of this technique are given in [54, 97]. Thereby, an ”artifi-cial”spectral spreading increases the resistance against interference of the trans-mitting signal. With a user dependent spreading code, this code multiplex tech-nique can be used as a multiple access scheme. This technology is called CodeDivision Multiple Access (CDMA). CDMA has been claimed to deliver high ca-pacity [105], i.e. that a lot of users can be served. The frequency reuse factor isone, therefore all cells can use the whole frequency band. Furthermore, CDMAoffers the possibility to combat interference efficiently and multipath propagationby the use of simple RAKE receivers. Details of the RAKE receiver are explainedin Appendix C.

The standardized UTRA solution from ETSI includes two different concepts.These two are the UTRA FDD mode with the use of W-CDMA (WidebandCDMA) and the UTRA TDD mode with TD-CDMA (Time Division/Code Di-vision Multiple Access). For the FDD mode, two 60 MHz bands (paired bands,1920 -1980MHz for UL and 2110 - 2170MHz for DL) are scheduled, whereas 20and 15MHz (unpaired band, 1900 -1920MHz and 2010 - 2025MHz) are reservedfor the TDD mode. The planned application areas for the FDD mode are publicmacro and micro cells with data rates up to 384 kbit/s, whereas the TDD modeshould be dedicated for small public cells (micro and pico cells) as well as forunlicensed wireless applications and wireless local loops (WLL) with data ratesup to 2Mbit/s. In the following the UTRA FDD mode is explained in moredetail.

9

10 CHAPTER 2. RADIO SYSTEM ASPECTS

2.2 UTRA FDD System Description

A short overview about the technical details of the FDD mode is given in Table2.1.

Multiple access scheme DS-CDMA

Duplex technique FDD

Modulation scheme QPSK

Chip rate 3.84 Mchip/s

Pulse shaping Root raised cosine with a

roll-off factor of 0.22

Bearer spacing 5MHz

Frame length 10ms

Synchronization between the base stations not necessary

Table 2.1: Technical overview of FDD mode.

The most important characteristics of the W-CDMA technology, which are usedwithin the FDD mode, are presented in the following list:

• Flexible support of different services. W-CDMA offers the possibility toallocate the required data rates by a judicious choice of different spreadingsequences and by the adaptation of the transmit power. The managementof the resources takes place in a decentralized way in each cell.

• Increased capacity and coverage by high bandwidth (interference resistancealso compared to the other users in the same cell) and fast power control.

• Assistance of adaptive antenna arrays, called Smart Antennas. The appli-cation of these antennas improves the interference resistance and thereforeboosts the capacity.

• The base stations operate asynchronously and so the expense for the in-stallation of the network is significantly reduced. It is also possible to useUMTS as a local network inside a building.

2.2.1 Channel and Frame Structure

As in the GSM system, the flow of information in the UMTS system is organizedin channels. Thereby, we differ between transport channels and physical channels.

2.2. UTRA FDD SYSTEM DESCRIPTION 11

The transport channels are mapped on the physical channels by multiplexing (seeFigure 2.1).

Figure 2.1: Channel structure for W-CDMA [97].

The transport channels are divided into common and dedicated transport chan-nels, whereby the first ones are used by more than one mobile in the cell. Thesame distinction is made for the physical channels. In the case of the dedi-cated physical channels we can differ between the dedicated physical data channel(DPDCH) and the dedicated physical control channel (DPCCH). The DPCCHincludes pilot bits, data for the transmit power control (TPC) and informationabout the transmitting data (TFI, transport format indicator). Figure 2.2 showsthe frame structure of the dedicated transport channel for the downlink. Eachframe has a length of 10ms and is divided into 15 slots. The first part of a slotincludes the DPCCH and in the second part follows the DPDCH. There are 7 dif-ferent bit rates possible, from 12.2 kbit/s up to 2Mbit/s. Due to the fact that thechip rate is constant (3.84Mchip/s), different spreading factors are necessary. Forthe increase of the data rate over 1Mbit/s, several DPDCHs are assigned to onemobile, but only one DPCCH. The frame structure of the DPCH for the uplinkis shown in Figure 2.3. In contrast to downlink, data and control information aretransmitted in parallel, separated by the I- and Q-branch (see Section 2.2.2). Adescription of the individual transport and physical channels and their mappingis given in Section 2.3.


Figure 2.2: Frame structure for the downlink DPCH.

Figure 2.3: Frame structure for the uplink DPCH.

2.2. UTRA FDD SYSTEM DESCRIPTION 13

2.2.2 Modulation, Spreading and Scrambling

Figures 2.4 and 2.5 show the arrangement of modulator, spreading, scrambling,pulse shaping and conversion to IF- and HF- frequency for uplink and downlink.

Figure 2.4: Transmit path for DPCH in the downlink direction.

Figure 2.5: Transmit path for DPCH in the uplink direction.

The spreading of the spectrum is accomplished by the channelization code cch.The channelization codes are the same for up- and downlink. Their task is toorthogonalize the different data streams of the transmitter and to increase thebandwidth. Due to the different data rates and the fixed chip rate of 3.84Mchip/sthere are various codes with different lengths. These codes have to be orthogonal,because in the downlink the separation of the users is done by the channelizationcodes. Therefore, so-called OVSF (orthogonal variable spreading factor) codes areused. The choice of the codes takes place with the code tree shown in Figure 2.6.

The codes of all branches from one layer of the tree are orthogonal to each other.Furthermore, all codes with different length are orthogonal to each other, if thebranch from the longer code does not originate from the branch with the shortercode.

The scrambling in the downlink takes place with a 38400 chip (10ms) long seg-ment of a gold code with the length 218− 1. This code is repeated in each frame.Overall, 512 different scrambling codes are used. Through scrambling no addi-tional spreading of the spectrum takes place. Figure 2.7 shows the relation of thechip rate in the channel to spreading and scrambling in UTRA. In downlink for


Figure 2.6: Code tree for OVSF (orthogonal variable spreading factor) codes.

each cell only one scrambling code is assigned (i.e for each mobile in the cell thesame code is applied). Therefore, the purpose of the complex valued scramblingcodes are to distinguish signals originating from different base stations in themobile.

The scrambling in the uplink is done in order to distinguish signals of differentmobiles at the base stations. There are two different possibilities for the scram-bling operation: either, a short code from a Kasami-set [19] of length 256 ora long code, consisting of a 38400 chip segment from a gold code [19] of length241−1. The possibility of utilization of the short code was created to facilitate theJoint Detection technique [19] with feasible effort. In contrast to the downlink,the separation of the users (i.e the orthogonality) in the uplink is guaranteedby the scrambling code and not by the spreading code. This means that a mo-bile, when choosing its spreading code, does not have to take into account thespreading codes of the other users in the cell.

Figure 2.7: Relation between spreading and scrambling.

2.3. TRANSPORT CHANNELS AND PHYSICAL CHANNELS 15

2.3 Transport Channels and Physical Channels

In UTRA FDD the data generated at higher layers is carried over the air interfacewith transport channels, which are mapped in the physical layer to differentphysical channels. The basic organization of this concept for the informationflow is described in Section 2.2.1 of this thesis. The various different tasks (e.g.cell search, paging,...) that the UMTS network has to fulfill are handled bydifferent transport channels. These ’logical transport channels’ are the ’services’that the physical layer offers to higher layers. Figure 2.8 illustrates the mapping oftransport channels to physical channels. There are also physical channels that arenot transparent to higher layers and have no corresponding transport channels.The synchronization channel (SCH), the common pilot channel (CPICH) and theacquisition indication channel (AICH) are not directly visible to higher layers,but are essential from the system function point of view. The dedicated channel(DCH) is mapped onto two physical channels, the dedicated physical data channel(DPDCH) and the dedicated physical control channel (DPCCH).

In the following a brief description of the different channels is given.

2.3.1 Synchronization Channel

The synchronization channel (SCH) is a pure physical channel used for the cellsearch procedure by the mobile. Hence, the SCH has to be transmitted intothe entire cell. The SCH consists of two sub-channels transmitted in parallel,the primary and secondary SCH. The 10ms radio frames of the primary andsecondary SCH are divided into 15 slots of length 2560 chips each. The SCH isonly transmitted during the first 256 chips of each slot. Figure 2.9 shows thestructure of the SCH.

2.3.1.1 Primary SCH

The primary SCH consists of a modulated sequence of length 256 chips, theprimary synchronization code, denoted cp in Figure 2.9. The sequence cp is trans-mitted once every slot and is the same for every cell in the system, so that themobiles can detect it easily with a matched filter.

2.3.1.2 Secondary SCH

The secondary SCH consists of a repeatedly transmitted sequence of modulatedcodes with a length of 256 chips. The secondary synchronization codes, which are


Figure 2.8: Transport-channel to physical-channel mapping.

Figure 2.9: Synchronization Channel (SCH) structure.


transmitted in parallel to cp, are denoted ci,ks in Figure 2.9, where i = 0, 1..., 63 is

the number of the scrambling code group, and k = 0, 1, ...14 is the slot number.Each secondary synchronization code is from a set of 16 different codes. Thesequence of secondary synchronization codes transmitted on the secondary SCHindicates which of the 64 possible scrambling code groups is used in the cell. Asthe sequence of the 15 secondary synchronization codes are rotationally variant,the secondary SCH determines also the beginning of the radio frame. Figure 2.11in Section 2.4.1 shows the 64 code groups.

2.3.2 Common Pilot Channel

The common pilot channel (CPICH) is a fixed rate downlink physical chan-nel (15 kbit/s) that carries a continuous pre-defined bit/symbol sequence. Thespreading factor of the CPICH is 256. Figure 2.10 shows the frame structure ofthe CPICH. The function of the CPICH is to aid the channel estimation at themobile for the DCH (see Section 2.3.7) and to provide the channel estimationreference for the common channels. The CPICH does not carry any higher layerinformation, neither is there any transport channel mapped to it.

Figure 2.10: Common Pilot Channel (CPICH) structure.

There are two types of CPICHs, the primary and secondary CPICH (P-CPICHand S-CPICH). They differ in their use and the limitations placed on their phys-ical features [3]. In the following the P-CPICH and S-CPICH are explained inmore detail.

2.3.2.1 Primary Common Pilot Channel

The primary common channel (P-CPICH) is always spread with channelizationcode Cch,256,0 and scrambled with the scrambling code of the cell. Hence the


P-CPICH can be used in the mobile to determine the scrambling code used forscrambling the downlink channels of the cell (see Section 2.4.1). There is alwaysonly one P-CPICH per cell, which is broadcast over the entire cell. The P-CPICHis the phase reference for the SCH, P-CCPCH, AICH and PICH, and the defaultphase reference for all other downlink physical channels [3].

The P-CPICH is responsible for the measurements on the handover and cellselection/reselection. The use of the CPICH reception level at the mobile forhandover measurements has the consequence that by adjusting the CPICH powerthe cell load can be balanced between adjacent cells. Reducing the CPICH powerin one cell causes that mobiles at the cell boundary hand over to the neighboringcells, while increasing it invites more mobiles to hand over to the cell [54].

2.3.2.2 Secondary Common Pilot Channel

The secondary common pilot channel (S-CPICH) can be spread by any chan-nelization code of length 256 and can be scrambled by either the primary or asecondary scrambling code. There maybe zero, one, or several S-CPICHs percell. A S-CPICH may be transmitted over the entire cell or only into a part ofthe cell. The S-CPICH can be the phase reference for secondary common controlphysical channels (S-CCPCH) and dedicated physical channels (DPCH) [3].

2.3.3 Broadcast Channel

The broadcast channel (BCH) is a transport channel that is used to broadcastnetwork or cell specific information for a given cell. The most typical data neededin every network are the available random access codes and access slots in thecell, or the types of transmit diversity methods used with other channels for thatcell. Physically the BCH is transported by the primary common control physicalchannel (P-CCPCH), which is a downlink data channel only. It is crucial thatall mobiles in the cell can decode the P-CCPCH. Therefore, the P-CCPCH istransmitted with high power and fixed data rate (spreading factor SF = 256) inorder to reach all the users within the intended coverage area.

2.3.4 Random Access Channel

The random access channel (RACH) is an uplink transport channel, which car-ries control information from the terminal, such as a request to set up an RNCconnection. It can further be used to send small amounts of uplink packet data to


the network [54]. The random access channel must be heard from the whole de-sired cell coverage area, especially for the initial system access and other controlprocedures.

The RACH is mapped on the physical random access channel (PRACH). ThePRACH has specific preambles, which are sent prior to data transmission. Theseuse a spreading factor of 256 and contain a signature sequence of 16 symbols.Once the preamble has been detected by the base station and acknowledged withthe acquisition indicator channel (AICH) the 10 or 20ms long message part istransmitted with a spreading factor from 256 down to 32. If the sent preambleis not acknowledged within a certain time, the preamble is retransmitted withincreased power until the reception of the preamble is acknowledged by the basestation. Beside this power adjustment prior to transmission, there is no powercontrol for the PRACH.

2.3.5 Forward Access Channel

The forward access channel (FACH) is a downlink transport channel that carriescontrol data to terminals in the given cell, for example, after a random access mes-sage has been received by the base station. It is also possible to transmit packetdata on the FACH. There can be more than one FACH per cell with differentdata rates, but there must be at least one FACH with such a low data rate thatall terminals in the cell can decode it. The FACH does not use fast power control,and the messages transmitted need to include in-band identification information.

The FACH is mapped on the secondary common control physical channel (S-CCPCH). If there exists more than one FACH in the cell, then all these aremultiplexed on one S-CCPCH. The S-CCPCH may use different offsets betweenthe control and data fields at different symbol rates and may support slow powercontrol. More details and a table of all possible slot formats are given in [3].

2.3.6 Paging Channel

The paging channel (PCH) is a downlink transport channel that carries datarelevant to the paging procedure, that is, when the network wants to initiatecommunication with the terminal [54]. An example is a speech call: the networktransmits the paging message to the terminal on the paging channel of those cellsbelonging to the location area that the terminal is expected to be in. The signalhas to be heard in the entire cell area and can be transmitted in up to a fewhundreds cells, depending on the system configuration. The PCH is transportedlike the FACH by the S-CCPCH.


The configuration of the paging channel affects the terminal’s power consumptionin standby mode. The less often the mobile has to tune the receiver on to listenfor a possible paging message, the longer will the terminals battery last in standbymode. Therefore, all mobiles are assigned to a paging group. The presence of apaging message in the S-CCPCH is signaled by the network on a separate physicalchannel, the paging indication channel (PICH). So, a terminal in stand-by modeonly has to check the PICH and does not need to decode the S-CCPCH all thetime.

2.3.7 Dedicated Channel

The dedicated channel (DCH) is the only dedicated transport channel specified in3GPP. The DCH carries all higher layer information intended for a given user in-cluding data for the actual services (speech frames, data,...) as well as higher layercontrol information (handover commands, measurement control commands,...).In the physical layer the DCH is mapped on the dedicated physical channel(DPCH). The DPCH uses closed-loop power control and fast data adaptation ona frame-by-frame basis. It can be transmitted to a part of the cell and supportsoft/softer handover. The DPCH consists of two sub-channels, the dedicatedphysical data channel (DPDCH) that carries the actual user data and the dedi-cated physical control channel (DPCCH) that carries physical layer information.

As the modulation scheme is different in up- and downlink, the structure of theDPCH is also different for the two directions. The downlink and uplink slotstructure of the DPCH are shown in Figure 2.2 and Figure 2.3 in Section 2.2.1.

2.3.8 Downlink Shared Channel

The downlink shared channel (DSCH) is a transport channel intended to carrydedicated user data and/or control information in the downlink. The DSCH canbe shared by several users [54]. As a pure data channel, the DSCH is alwaysassociated with a downlink DCH for power control and signaling purposes. Inthe physical layer the DSCH is mapped on the physical downlink shared channel(PDSCH). The PDSCH uses fast power control and the data rate can be changedon a frame-by-frame basis.

2.3.9 Uplink Common Packet Channel

The uplink common packet channel (CPCH) is an extention to the RACH thatis intended to carry packet-based user data in the uplink direction. In contrast

2.4. PHYSICAL LAYER PROCEDURES 21

to the RACH the CPCH uses fast power control and collision detection. In thephysical layer the CPCH is transported by the physical common packet channel(PCPCH). The uplink CPCH transmission may last serveral frames in contrastwith one or two frames for the RACH meassage. A detailed description of theCPCH can be found in [54].

2.3.10 Necessary Common Channels in a UMTS Network

For the basic network operation the following channels are required: SCH, P-CPICH, the P-CCPCH for carrying the BCH, a S-CCPCH for carrying the FACHand the PCH, the PICH and a PRACH together with AICH for random access.The use of the CPCH and DSCH is optional for the network, which makes allphysical channels necessary for signaling and transport of CPCH and DSCHoptional.

2.4 Physical Layer Procedures

In the physical layer of the UMTS system there are many procedures essential forsystem operation. Power control, cell search and handover are briefly describedin this section. An exhaustive description can be found in [54, 68].

2.4.1 Cell Search

The cell search procedure or synchronization procedure in UTRA FDD mode ofthe UMTS system differs greatly from the procedure in a synchronous systemlike cdma2000. Since the cells use 512 different scrambling codes and not justdifferent code phase shifts, terminals cannot search all the codes of 10ms durationwithout any prior knowledge.

The cell search procedure using the synchronization channel has basically threesteps:

• Step 1: Slot synchronizationAfter ’power on’ the mobile searches for the 256-chip primary SCH that isequal in all cells. As the primary SCH code is the same in every slot, thepeak detected corresponds to the slot boundary.

• Step 2: Frame synchronization and code-group identificationIn the second step the mobile detects the secondary SCH in order to de-termine the boundaries of the frame structure and which of the 64 possible


Figure 2.11: Code groups.

code groups is used for the downlink scrambling. In the example in Fig-ure 2.11 the cell uses the code group with the number 4.

• Step 3: Scrambling-code identificationDuring the third and last step of the cell search procedure, the mobiledetermines the exact primary scrambling code used by the found cell. Thisis done by trying to detect the P-CPICH that is scrambled with one ofthe eight primary scrambling codes of the code group. After the primaryscrambling code has been identified, the primary CCPCH can be detectedand the system- and cell specific BCH information can be read.

2.4.2 Power Control

Stringent uplink and downlink transmit power control (TPC) is required to com-bat the near-far problem. The near-far problem in the uplink is created by inter-fering users located near the base stations, which corrupt the signals from moredistant mobiles. The uplink TPC must be fast enough to track rapid channelvariations (e.g. caused by small-scale fading). The same properties are desiredfor the downlink TPC even though the near-far problem is less important. InUTRA FDD mode the power control is done in the control loops, the outer loopand the inner loop. The outer loop is handled in the RNC and controls the QoS(e.g. BER or BLER) by adjusting the target signal to interference ratio (SIR)of the link according to the service. The SIR target is adjusted at a slow rate(typically 10-100Hz) and signaled via higher layers. The required SIR, BER andBLER measurements are standardized in [4].

The inner loop of the power control, also called fast power control or closed looppower control, adjusts the transmit power after every slot (666 µs) according tothe received SIR of the previous slot, resulting in a 1500Hz command rate. Ifthe received SIR is below the target SIR, then a power up command is fed back

2.4. PHYSICAL LAYER PROCEDURES 23

to the transmitter using the power control bits that are reserved for this purposein every slot. If the received SIR is equal to or above the target SIR, then apower down command is sent to the transmitter. The transmitter adjusts thetransmit power according to the received power control command in steps of 1 dB.Additionally, multiples of that step size can be used. The specifications definethe relative accuracy for a 1 dB power control step to be ±0.5 dB. Figure 2.12shows the interaction between the outer and inner loop.

Figure 2.12: Outer and inner TPC loop. The green-colored blocks reside in thephysical (PHY) layer and the yellow-colored blocks reside in the radio resourcecontrol (RRC) layer (source: [74]).

In UTRA FDD there is also an open loop power control, which is applied for ini-tiating the transmission on the RACH or CPCH. Here the transmitter estimatesthe pathloss in the downlink using the received P-CPICH power (the transmitP-CPICH power is known at the receiver) prior to transmission. The mobile ad-justs the transmit power using the noise level at the receiver that is signaled onhigher layers in such a way that the target SIR is reached at the receiver. Dueto measurement inaccuracies the open loop power control is very inaccurate. Innormal conditions the tolerance for the open loop power control is ±9 dB [6].

2.4.3 Handover

Handover is one of the most important mechanisms in wireless networks, since itprovides the maintenance of seamless communication when a mobile moves fromone site to another. Within the UTRA FDD mode the possible handovers are asfollows:


• Softer handover

• Soft handover

• Hard handover

During softer handover, a mobile is simultaneously connected to two adjacentcells of a site (base station). The communication between the mobile and thebase station takes place concurrently via two air interface channels, one for eachcell separately. This requires the use of two separate codes in the downlink. Inthe uplink a similar process takes place at the base station: the signal of themobile is received in each cell and then combined by maximum ratio combining1.Softer handover typically occurs in about 5 - 15% of the connections [54]. Figure2.13 shows a softer handover scenario.

Figure 2.13: Softer handover.

During soft handover, a mobile is connected to two cells belonging to differentbase stations. As in softer handover, the communication between the mobile andthe base stations take place concurrently via two air interface channels from eachbase station separately. As in softer handover, both signals are received at themobile by maximum ratio combining rake processing. Seen from the mobile, thereare very few differences between softer and soft handover. However, in the uplinksoft handover differs significantly from softer handover: the signals of the mobileare received from both base stations, but the received data is then routed to theRNC for combining. Soft handover occurs in about 20 - 40% of the connections[54]. Figure 2.14 shows a soft handover scenario.

In addition to soft/softer handover, the UTRA FDD mode provides two otherhandover types (hard handover2):

• Inter-frequency hard handover: Occurs for example, when a mobile handsover from one W-CDMA frequency carrier to another. One application for

1Maximum ratio combining (MRC) is one of the most common linear combining techniquesin receive diversity systems. In MRC, combiner weights are chosen to maximize the outputsignal to noise ratio (SNR). Details on MRC are well described in [59, 90].

2A hard handover results in the radio connection being broken between the network and themobile, before a new radio connection is established with the network in the target cell. Hardhandovers usually require a change of frequency.

2.5. SERVICES AND APPLICATIONS 25

Figure 2.14: Soft handover.

this are high capacity base stations, so-called hot-spot cells3, with severalcarriers. Another application is the handover from a macro-cell to a micro-cell, which uses different frequencies.

• Inter-system hard handover: Takes place between the UTRA FDD modeand the UTRA TDD mode, or between the UTRA FDD mode and theGSM system.

The support of seamless inter-frequency hard handover is a key feature of W-CDMA, not previously implemented in cellular CDMA system. Hard handover isnecessary for the support of a hierarchical cell structure (HCS): a cellular systemcan provide very high capacity through micro-cells, offering at the same timefull coverage via the macro cells. Therefore, hard handover is a very importantfeature to perform handover between the different cells. A second scenario, wherehard handover is necessary, is the hot-spot one. In this case, a certain cell thatserves a high traffic area uses carriers in addition to those used by the neighboringcells. If the deployment of extra carries is to be limited to the actual hot-spotarea, the possibility of hard handover is essential.

2.5 Services and Applications

In second generation mobile communication systems like GSM, basically there isonly one major service, speech (circus switched), from which a network cannotoffer many services and applications to a demanding user. UMTS will be ableto supply a wide range of services with different bit rates and flexible trafficasymmetry.

3Hot spot cells can have a larger number of carriers than the surrounding ones, therefore,a different mechanism of handover is necessary between different frequencies, which is hardhandover.


In this section two classifications of services from different points of view arepresented. The first classification is based on market forecasts by the UMTSForum. The second one is based on QoS requests. Further, the importance oftraffic forecasts for network optimization is explained.

2.5.1 UMTS Forum Traffic Classes

Based on market forecasts, which are performed by the UMTS Forum (see Sec-tion 1.2) the services for UMTS can be divided in several classes. Figure 2.15shows this classification.

Speech (S) is a symmetric service with the same amount of information in theUL as in the DL and with an activity factor of 0.5. This implies that the systemshould be able to handle the discontinuous transmission mode. The simple mes-saging service (SM) is the evolution of the GSM short message service (SMS).The typical size of a message is about 40KByte and and an acceptable delayfor this service is about 30 s. The switched data service is a 14.4 kbit/s CS ser-vice type similar to existing data service in GSM. Services like downloads for theWWW belong to the multimedia service class (MM). The typical amount of datathat needs to be transmitted for a medium MM service is about 0.5MByte during14 s, while for a high MM service a data size of 10MByte and a call duration of53 s is typical. While these MM services are asymmetrical, the interactive MMservice is based on a 128 kbit/s symmetrical connection.

Services User Effective User net Coding Asymmetry Switch Service

nominal bit call bit rate factor factor mode bandwidth

rate [kbit/s] duration [s] [kbit/s] [kbit/s]

HIMM 128 144 128 2 1/1 CS 256/256

HMM 2000 53 1509 2 0.005/1 PS 15/3200

MMM 384 14 286 2 0.026/1 PS 15/3200

SD 14 156 14.4 3 1/1 CS 43/43

SM 14 30 10.67 2 1/1 PS 22/22

S 16 60 16 1.75 1/1 CS 28/28

Table 2.2: Service Characteristics (source: [101]).

Table 2.2 shows UMTS service characteristics, where some major service parame-ters for a possible traffic and capacity estimation are presented. These parameterscan be explained as follows [47]:

• User nominal bit rate corresponds to the output bit rate from the sourcewithout error protection.


Figure 2.15: Types of services (source: [47]).


• Effective call duration of a service corresponds to how long, on average, theservice is connected. It is based on the average call duration multiplied bythe activity factor 4 (see Table 2.3) implies that the system should be ableto handle the discontinuous transmission (DTX) mode.

• User net bit rate is a measure of the bit rate taking into account the packetefficiency factor, which is based on considerations of practical packet net-works and includes the effect of retransmission of unsuccessful packets.

• Coding factor is a generalized measure of the degree of coding required totransport the service to the required quality.

• Asymmetry factor is used to show that some services will have a differentload (bit rate and bandwith) in the UL and DL.

• Switch mode defines, if the service is CS or PS. Since the call duration andthe activity factor are not suitable to characterize PS services, an estimationof effective call duration is generated.

• Service bandwith is the product of user nominal bit rate, coding factor andasymmetry factor.

The various classes have different characteristics. HIMM, e.g., video telephony,require isochronous transmission, as well as SD and S. Therefore, they are handledas CS services. The average call duration time of these services corresponds tothe actual connection set up time, and the effective call duration depends onthe activity factor, which is 0.5 for speech and 0.8 for video telephony. For PSservices, the call duration is the sum of the time intervals, where data is actuallytransferred via the air interface. Thus, the activity factor in this scenario isequal to one. In Figure 2.16 the structure of a PS transmission is shown and theeffective call duration per service according to the activity factor and the averagecall duration is given in Table 2.3.

The call duration and the activity factor are not suitable to characterize PS ser-vices. However, an estimation of the effective call duration, and the equivalentoffered bit quantity that packet services will generate, can be based on calcula-tions that consider busy hour calls and an acceptable throughput and delay forpacket services [96].

4The activity factor describes how many percent of a connection during a call one user is,on average, active. Or in other words, it describes if and how much, on average, the activity ofthe service will vary.


Figure 2.16: Packet transmission over the UMTS air interface (source: [100]).

Services Activity factor Average call duration [s] Effective call duration [s]

HIMM 0.8 180 144

HMM 1 53.3 53.3

MMM 1 13.9 13.9

SD 1 156 156

SM 1 30 30

S 0.5 120 60

Table 2.3: Effective call duration (source: [101]).


2.5.2 3GPP UMTS QoS Classes

Beside the classification in the six groups S, SM, SD, MMM, HMM and HIMMfrom the UMTS-Forum described above, there exists also a second service clas-sification. 3GPP divides the applications and services in four different groups,according to the QoS requests. The four different traffic classes are: conversa-tional, streaming, interactive, and background. The main distinguishing factorbetween these classes is the delay-sensitive of the traffic: the conversational classis meant for very delay-sensitive traffic, while background class is the most delay-insensitive. The characteristics of the UMTS QoS classes are shown in Figure2.17.

Figure 2.17: 3GPP traffic classes classification.

The conversational and streaming classes are typically transmitted as real-timeconnections over the air interface, while interactive and background classes aretransmitted as non-real-time packet data using packet scheduling. The applica-tions, which belong to the four different classes, are shown in the following list[54]:

• Conversational: Real time applications (speech services, voice over IP, videotelephony), strict low end-to-end delay.

• Streaming: Streaming data transferring applications (web broadcast, videostreaming on demand), with high symmetric traffic.

• Interactive: Client-server applications (web browsing, database access, games,tele-machines), low round trip delay is required

• Background: Long delay applications (SMS, email, downloading databases,...)

A detailed description of the four UMTS QoS classes can be found in [54].


2.5.3 Traffic Forecast for UMTS Network Optimization

For radio network optimization, traffic is a very important input parameter.Therefore, it is necessary to perform some traffic forecast based on users andservices statistics. Table 2.4 shows statistical information that is fundamental forthe network optimization process, since most of traffic estimation is dependenton the user density. Therefore, the corresponding network capacity estimationfor each operational environment may be obtained. Only three of the operationalenvironments (marked in bold in Table 2.4) contribute to the maximum totalamount of capacity required, because they coexist in the same geographical area[96].

Operational environments Density of potential users/km2 Cell type

CBD/urban (in building) 180 000 Micro/pico

Suburban (in building or on street) 7 200 Macro

Home (in building) 380 Pico

Urban (pedestrian) 108 000 Macro/micro

Urban (vehicular) 2 780 Marco/micro

Rural in- and out-door 36 Macro

Table 2.4: Operational environment and cell types (source: [101]).

The following conclusions are based upon market forecast data for the years upto 2005 [101]. For example, it is assumed that 90% of the total speech andlow data traffic will be carried over existing 2G networks within this period.It is also considered that 60% of the indoor traffic will be carried over license-exempt networks (like WLAN), and that high (2Mbit/s) and medium (384 kbit/s)multimedia services are PS, which are tolerant to delay. Although the majority ofusers will be speech users, most of the capacity is needed for multimedia services.


Chapter 3

Overview of OptimizationTechniques

3.1 General Issues

This chapter gives a short overview of commonly used and well known optimiza-tion techniques. There are several different techniques, which are suitable fordifferent optimization problems. The different types of optimization problemsare classified according to [92] in the following list:

• Continuous functions

• Combinatorial problems

• Nonlinear structures, e.g.:

– Neural networks

– Fuzzy systems

– Computer programs

– Integrated circuits

The problem of optimizing the base station parameters of a UMTS network,which is covered by this work, is a highly nonlinear problem and is assigned tothe first item in the previous list. For an optimization problem a quantitativevalue (fitness function) is defined, for which we are interested in the maximum orminimum value. This value depends on different parameters, and the goal of anoptimization algorithm is to optimize the quantitative value (fitness function).The optimization algorithms can be classified according to their tasks in thefollowing way [83]:

33

34 CHAPTER 3. OVERVIEW OF OPTIMIZATION TECHNIQUES

• Restricted and not restricted optimization:In the case of a restricted optimization problem limits are set for the vari-ables, e.g. only positive values are allowed for a variable.

• Global and local optimization:Global optimization looks always for the absolute maxima or minima of afunction. In contrast, local optimization, only considers the maximum orminimum value within a limited scope.

• With or without calculation of the derivation:If it is possible to calculate the derivation, it can be involved in the op-timization process. Often, the calculation of the derivation is very timeconsuming, especially when the solution space is very large.

• One-dimensional and multidimensional problems:In the case of one-dimensional problems the optimization algorithm onlyhas to optimize one parameter. On the other hand, multidimensional opti-mization has to optimize more than one parameter. In most practical tasksthis is the case.

• Continuous and discrete optimization:Discrete optimization deals with parameters with discrete values (e.g zeroand one) and so the solution space is limited. In the case of a continuousoptimization the parameters are real values and so there exists an infinitenumber of solutions.

Examples of conventional optimization methods are Random Search, Simplex-method, enumeration method and gradient method. Random Search is a globalmethod and very generally applicable, but on the other hand usually inefficient.The Simplex-method is an approach involving mathematical programming for-mulations, which is suitable for linear fitness functions with linear boundary con-ditions. The third conventional method, the enumeration method, is a primitiveenumeration of the solutions. It is very inefficient, especially in the case of a largesolution space. For this reason advanced enumeration methods were developed,like dynamic programing or branch-and-bound. The gradient method is a verypopular approach for several types of problems. It is a purely local method, andit must be possible to calculate the derivation. For an overview on this area see[43].

For solving complex problems, like nonlinear structures, it is very inefficient andtime-consuming to use such conventional mathematical methods for optimizingthese kind of problems. Some problems are intractable to solve due to the fol-lowing properties, which can occour:

• The solution space is large and complex.

3.1. GENERAL ISSUES 35

• The function to be optimized has many of local optima. Such a function iscalled multi-modal [76].

• Boundary conditions complicate the search for the optima.

• The optimized function is time-variant or superposed with noise.

• The problem has more than one fitness function. Therefore, a set of Pareto-optimal1 [76] solutions is searched.

In the last decades also other, now commonly applied optimization methodshave been developed, which not necessarily find the absolute optimum, but theyhopefully give a good approximate solution. The algorithms can be summarizedunder the term heuristic methods. One class of these heuristic methods isvery popular for solving practical problems, the so-called meta-heuristics. In thefollowing list the most commonly used meta-heuristic techniques are shown:

• Local Search

• Tabu Search

• Simulated Annealing

• Genetic Algorithms

• Evolutionary Strategies

• Evolutionary Programming

• Estimation of Distribution Algorithms

• Ant Colony Optimization

While business and operations research practitioners have frequently focused onconventional optimization approaches involving mathematical programming for-mulations, computer scientists and electronic engineers have focused on apply-ing optimization techniques subject to multi-criteria objectives and constraints[106]. Under mathematical programming formulations, discrete decision variablesare used to represent components of the model and/or network. Subsequently,maximization objectives and constraints are stated in terms of (frequently lin-ear) equations. Such problems are often tackled using commercial mathematical

1A solution is called Pareto-optimal (or efficient) solution, if there is no other solution forwhich at least one criterion has a better value while values of remaining criteria are the sameor better. In other words, one can not improve any criterion without deteriorating a value ofat least one other criterion.


programming solvers (such as CPLEX, using a Simplex-method in [73] for basestation positioning for cellular radio networks) or using problem specific heuris-tics and techniques. An important difference between these approaches concernsthe required effort. Formulating a problem so that mathematical programmingcan be applied may be problematic for engineering problems such as network de-sign, particularly when non-linearities and dependencies need to be considered.It takes potentially much less effort to tailor a suitable meta-heuristic approachsuch as Simulated Annealing. However, given the difficulty of the network designproblem and the tensions and conflicts involved, the way in which meta-heuristicstechniques are targeted and tailored is crucial.

In this chapter a short outline for some of the most effective and frequently usedmeta-heuristic solution techniques are introduced. A comprehensive overview ofthese and other relevant techniques is given in [86].

3.2 Local Search

The Local Search algorithm is a local iterative search technique that generatesa sequence of solutions for the optimization problem, say x1, x2, . . . , xk. At eachiteration i (1 ≤ i ≤ k − 1), solution xi is perturbed a number of times (by ruleswhich are described as a move) to produce a neighborhood N(x) of candidatesolutions. The best solution in the neighborhood is then taken as solution xi+1,if it is better than the solution in the previous iteration. This algorithm workswith the so-called greedy concept, because it always takes the best one. In Figure3.1 a simple program structure for a local search algorithm is shown.

procedure Local Searchbegin

x← initial solution;repeat

x′ ← N(x); // derive neighborhood solutionif x′ is better than x then

x← x′;until termination condition is fulfilled;

end

Figure 3.1: Local Search algorithm.

One big disadvantage of the algorithm is that in general it only finds a localoptimum. An improvement gives the so-called Multi-start Local Search, whichstarts from several different initial solutions. A full description of the Local Search

3.3. SIMULATED ANNEALING 37

method and its extensions is given in [8, 85]. For the optimization of combinatorialproblems it is a very popular approach. The best known example is the Traveling-Salesman Problem2. Also for network design such methods are utilized. Thisapproach is best to generate an initial network for further development [106]. Acrucial issue concerns the way in which the candidate sites3 for a network andconfigurations are ordered. Such methods, for example, have been investigatedfor related graph based problems and frequency assignment for GSM in [14, 15].In [99], this approach has been used for cell planning.

3.3 Simulated Annealing

Simulated Annealing (SA) is a meta-heuristic derived from statistical mechanics.It is a technique that has attracted significant attention as suitable for optimiza-tion problems of large scale, especially ones where a desired global extremum ishidden among many, poorer, local extrema.

At the heart of the method of Simulated Annealing is an analogy with thermo-dynamics, specifically with the way that liquids freeze and crystallize, or metalscool and anneal. At high temperatures, the molecules of a liquid move freelywith respect to one another. If the liquid is cooled slowly, thermal mobility islost. The atoms are often to line themselves up and form a pure crystal that iscompletely ordered over a distance up to billions of times the size of an individualatom in all directions. This crystal is the state of the minimum energy for thissystem. The amazing fact is that for slowly cooled systems, nature is able to findthis minimum energy state. In fact, if a liquid metal is cooled quickly, it doesnot reach this state. So the essence of the process is slow cooling, allowing ampletime for redistribution of the atoms as they lose mobility. This is the technicaldefinition of annealing, defined in [89].

The so-called Boltzmann probability distribution,

Prob (E) ∼ e−E/kT (3.1)

expresses the idea that a system in thermal equilibrium at temperature T hasits energy probabilistically distributed among all different energy states E. Evenat low temperature, there is a chance, albeit very small, of a system being ina high energy state. Therefore, there is a corresponding chance for the systemto get out of a local energy minimum in favor of finding a better, more global

2The traveling salesman problem, or TSP for short, is defined as follows: given a finitenumber of ”cities”along with the cost of travel between each pair of them, find the cheapestway of visiting all the cities and returning to your starting point.

3A candidate site is a possible site for a base station.


one. In Equ. (3.1) the quantity k (Boltzmann’s constant) is a constant of naturethat relates temperature to energy. In other words, the system sometimes goesuphill as well as downhill ; but the lower the temperature, the less likely is anysignificant uphill excursion. For the simulation of the anneling process finding astate of minimum energy the instructions are given in the following list:

• Start with high temperature and cool down slowly.

• For each temperature accept transitions to a new molecular structure, ifthe energy E is lower.

• Accept transitions to molecular structures with higher energy only with aprobability of e−E/kT .

The instructions in the list above, can be used as optimization algorithm. Theanalogy between the thermodynamic simulation of annealing and optimization isshown in Table 3.1.

thermodynamic simulation optimization

system state valid solution x

energy E value of fitness function f(x)

transition of state derive neighborhood solution N(x)

solidification (crystal) founded solution

temperature T control parameter T

Table 3.1: Analogy between the thermodynamic simulation of annealing andoptimization.

A simple program structure of a Simulated Annealing optimization algorithmis shown in Figure 3.2. The variable Z is an equal distributed random valuebetween zero and one. The critical point of the algorithm is how to choose theinitial value for the temperature T and the function for the cooling temperatureg(T, i). This function describes the cooling plan in the algorithm. In literaturethe following different functions are known among others:

• Geometric Cooling : g(T ) = αT with α ∈ [0.8, 1).

• Slow Cooling : g(T ) = T/(1 + βT ) with β > 0 and small.

• Theoretic good scheme: g(T ) = ∆/(1 + i) with ∆ proportional to the in-crease in fitness function, and i is the iteration counter.

3.4. TABU SEARCH 39

• Reannelaing : If a new solution is accepted, drop T according to g(T ) =T/(1 + βT ). If the new solution is discarded, increase T according tog(T ) = T/(1 + βT/l) with l ∈ N .

For more details on the different functions for T see [66]. The Simulated An-nealing optimization algorithm also accepts worse results in contrast to the LocalSearch described in 3.2, to permit escape from local optima which are not global.The chances of accepting a worse solution is controlled by the cooling tempera-ture T . For a detailed treatment of Simulated Annealing see [9]. This approachis very popular for cell planning issues [11, 56, 73].

procedure Simulated Annealingbegin

i← 0;T ← initial temperature;x← initial solution;repeat

x′ ← N(x); // derive neighborhood solutionif x′ is better than x then

x← x′;else

if Z < e−|f(x′)−f(x)|/T thenx← x′;

endT ← g(T, i);i← i + 1;

until termination condition is fulfilled;end

Figure 3.2: Simulated Annealing algorithm.

3.4 Tabu Search

The Tabu Search meta-heuristic technique (TS) operates using the neighborhoodprinciple of the Local Search technique from 3.2. However, in order to preventcycling and to provide a mechanism for escaping locally but not globally optimalsolutions, some moves4 at one particular iteration may be classified as tabu. Aftera valid move is executed, it is stored in the so-called tabu list TL. Now it is notallowed to perform this move again for the next k iterations. This tabu list is an

4A move denotes the transition from one solution to an other.


essential component of the algorithm, because it stores the history of the visitedcandidate solutions. There can also be aspiration criterias, which override thetabu moves if particular circumstances apply. A detailed description on tabu listmanagement and aspiration criteria can be found in [67].

procedure Tabu Searchbegin

TL← 0; // tabu listx← initial solution;repeat

X ′ ← subset of N(x) under consideration of TL;x′ ← best solution of X ′;add move from x to x′ to TL;delete moves from TL, which are older than k iterations;x← x′;if x is better than best solution until now then

store x;end


Figure 3.3: Tabu Search algorithm.

In Figure 3.3 a simple program structure for a Tabu Search algorithm is shown.The Tabu Search technique as extension of the Local Search algorithm is a veryefficient optimization technique for the optimization of combinatorial problemslike the TSP or the Bin Packing Problem5. In [18] a short overview of differentcombinatorial problems is given. For the TSP the Tabu Search algorithm nowa-days is the most efficient technique for optimizing this problem. For cell planningissues in CDMA networks this method is also used [12, 71]. For further detailsof Tabu Search see [49, 67, 93].

3.5 Evolutionary Algorithms

Evolutionary Algorithms (EA) are a category of computer-based problem solvingsystems, which use calculable models of natural selection and evolution processesas their key elements. The theory about evolution was founded by Charles Dar-

5Bin Packing Problem: Determine how to put the most objects in a given number of fixedspace bins. More formally, find a partition and assignment of a set of objects such that aconstraint is satisfied.

3.5. EVOLUTIONARY ALGORITHMS 41

win6 in the year 1859. In his famous book ”The Origin of Species ” he explainedthe inheritance with variances (mutation) and talked about natural selection.The principal structure of an evolutionary optimization algorithm in shown inFigure 3.4. In this Figure, P denotes a population of solutions. In each itera-tion a new generation Q is produced, and from the union of Q and P the newpopulation P is decided.

procedure EAbegin

P ← set of initial solutions;Evaluate(P );repeat

Q← GenerateNewSolutionByVariation(P );Evaluate(P );P ← SelectBetterSolutions(P, Q);


Figure 3.4: The structure of an Evolutionary Algorithm.

Evolutionary Algorithms have the following properties:

• They do not require certain properties of the optimized function like conti-nuity, differentiability or dimensionality.

• The algorithms are not restricted to numeric optimization.

• No special information about the solution space like derivation, is needed.

• Suitable for problems with a big solution space, where other techniques,like enumeration methods, need to much time.

• Global view of the technique: The algorithm searches for the global opti-mum, and not for the next local one.

• Finding of the optimal solution cannot be guaranteed.

• It is possible to combine Evolutionary Algorithms with other optimizationtechniques or problem specific heuristics.

6Charles Robert Darwin (1809-1882): Darwin was the British naturalist who became famousfor his theories of evolution and natural selection. Like several scientists before him, Darwinbelieved all the life on earth evolved (developed gradually) over millions of years from a fewcommon ancestors.


• Robustness: Evolutionary Algorithms are suitable for many different typesof problems (see Section 3.1). Specialized methods often achieve betterresults only on certain problems.

The most important types of Evolutionary Algorithms are enumerated in thelist below. This classification is not unique, because there are a lot of hybridapproaches.

• Genetic Algorithms

• Evolutionary Strategies

• Evolutionary Programming

• Classifier Systems

• Genetic Systems

• Genetic Programming

In the following the most important and most popular representatives, the Ge-netic Algorithms, which are used in this thesis for optimizing the base stationparameters, are described in more detail.

3.5.1 Genetic Algorithms

The idea of using genetic approaches for optimization originated from J. H. Hol-land about thirty years ago. He wrote the primary work about Genetic Algorithms(GA) [53]. De Jong [31] extended his work for functional optimization, involvingthe use of optimization search strategies based on after the Darwinian notion ofnatural selection and evolution. In the last two decades, first D. E. Goldberg [50]and then Z. Michalewicz [13, 76, 77] enhanced the methods and made researchon the theoretical basics of Genetic Algorithms.

Genetic Algorithms are particularly effective, when the goal is to find an approx-imate global maximum in a high dimensional, multi-modal function domain ina near-optimum manner [61]. They differ from other conventional techniques byoperating on a group (or population) of trial solutions in parallel. Normally aGenetic Algorithm operates on a coding of the function parameters (a chromo-some) rather than on the parameters themselves. For the coding of the problem asuitable structure should be used. The most common coding schemes are binarycoding or gray coding. Simple, stochastic operators (selection, crossover and mu-tation) are used to explore the solution domain in search of an optimal solution.The basic block diagram with the three operators are depicted in Figure 3.5.


Figure 3.5: Genetic Algorithm.

procedure GAbegin

i← 0;initialize(P (i));evaluate (P (i));while (not termination-condition) do

i← i + 1;Qs(i)← select(P (i− 1));Qr(i)← recombine(Qs(i));P (i)← mutate(Qr(i));evaluate (P (i));

doneend

Figure 3.6: Canonical Genetic Algorithm.


Figure 3.6 shows the algorithm of the block diagram from Figure 3.5. This simpletype of GA is known as Canonical GA. In Figure 3.6 P (i) denotes the populationof the actual iteration, Qs(t) indicates the population after the selection processand Qr(t) is the population after recombination. In keeping with the naturalselection analogy, successive populations of trial solutions are called generations.Subsequent generations are made up of children, produced through the selectivereproduction of pairs of parents taken from the current generation. A list of someof the commonly encountered GA terms relating to the optimization problem ispresented below:

Population: Set of trial solutions.

Parent: Member of the current generation.

Child: Member of the next generation.

Generation: Successively created populations (GA iterations).

Chromosome: Coded form of a trial solution vector (string) con-sisting of genes made of alleles. A chromosom isalso referred to as individual.

Gene: Each gene can have a value (allele) of a certainvalue set (e.g. one bit).

Allele: Concrete value of a gene (e.g for a bit representa-tion: zero or one).

Fitness: Positive number assigned to an individual repre-senting a measure of quality.

In the beginning of the optimization process the first population has to be initial-ized (see Figure 3.6). Normally this is done by a random setting. For the eval-uation of all the individuals of the population, a fitness function f(i) is needed.A higher fitness value means a better solution and a lower fitness value shows aworse solution. If the fitness function reaches the highest value, this means thatthe searched optimal solution is found.

In the following the three operators are shortly explained.

3.5.1.1 Selection

The selection of the parents for the next generation is mostly controlled by ran-domness, but according to the natural selection: Better individuals are selectedmore often then worse individuals. The selection process forces the populationof the GA in the direction of better solutions. In the majority of cases a fitnessproportional selection scheme is used. This method is referred to as roulette-wheel


selection, because it works like the roulette game with different probabilities. Foreach individual the fitness value is scaled according to the sum of the fitness of allthe individuals. The probability for an individual i to be selected in the selectionprocess is shown in Equ. (3.2).

ps(i) =f(i)∑n

j=1 f(j)(3.2)

with f(j) ≥ 0 andn∑

j=1

f(j) > 0

In Equ. (3.2) n denotes the size of the population.

3.5.1.2 Recombination

Recombination is the primary operator for generating new individuals. In Fig-ure 3.7 the 1-point crossover is shown, as one possible example for a recombina-tion operator. In this example 8 parameters are binary coded on one chromosome.

Figure 3.7: 1-point crossover.

The crossover is either performed for all individuals of the population, or for themajority of the population controlled by a certain randomness (e. g. pc = 0.8).The creation of couples as well as the choice of the crossover points are alsoperformed randomly.

3.5.1.3 Mutation

The mutation operator is used as secondary operator in Genetic Algorithms.This operator makes small, random changes. Only one gene of a chromosomeobtains a new value. Mutation is responsible for introducing new and lost genematerial (alleles) into the population. In Figure 3.8 an example of mutation fora chromosome with eight binary coded parameters is shown. Parameter no. 4mutates from 1 to 0.

The mutation is usually performed with a ceratin probability only (e.g. pm = 0.5).


Figure 3.8: Mutation.

3.5.1.4 Applications for Genetic Algorithms

Researchers and developers use Genetic Algorithms in many areas where a globaloptimum is searched for. In particular they are used for practical optimizationapplications with a big and complex solution spaces. In the field of electromag-netics engineering, developers use Genetic Algorithms for example for the designof lightweight, broadband microwave absorbers, the reduction of array sidelobesin thinned arrays, the shaped-beam antenna arrays or the design of broadbandpatch antennas [61].

In the area of network planning for 2G and 3G systems, Genetic Algorithmsare very popular. In [52, 78] these algorithms are used for the base stationlocation problem. The papers [21, 55, 70] demonstrate approaches for coverageand capacity optimization in wireless networks using Genetic Algorithms.

3.6 Ant Colony Optimization

The idea of imitating the behavior of ants to find solutions for combinatorialoptimization problems was initiated by Colorni, Dorigo and Maniezoo in 1991[24, 25, 26]. The metaphor comes from the way ants search for food and findtheir way back to the nest. Initially, ants explore the area surrounding their nestin a random manner. As soon as an ant finds a source food, it evaluates theinterest of the source (quality and quantity) and carries some of the food to thenest. During the return trip, the ant leaves a chemical pheromone trail on theground, whose quantity depends on the quality of the source. The role of thispheromone trail is to guide other ants towards the source. After a while, thepath to a good source of food will be indicated by a strong pheromone trail, asthe trail grows with the number of ants that reach the source. Since sources thatare close to the nest are visited more frequently than those that are far away,pheromone trails leading to the nearest sources grow faster. The final result ofthis process is that ants are able to optimize their work.

The transfer of this food searching behavior into an algorithmic framework for

3.6. ANT COLONY OPTIMIZATION 47

solving combinatorial optimization problems is obtained through an analogy be-tween:

• the search area of the real ants and the set of feasible solutions to thecombinatorial problem;

• the amount of food associated with a source and the fitness function;

• the pheromone trail and an adaptive memory.

A standard ant system is schematically described as follows:

1. Initialize the pheromone trail.

2. While a stopping criterion is not met do:

(a) for each ant, construct a new solution using the current pheromonetrail and an evaluation of the partial solution being constructed;

(b) update the pheromone trail.

The most important component of an ant system is the management of pheromonetrails. In a standard ant system, pheromone trails are used in conjunction withthe fitness function to guide the construction of new solutions. Once a solu-tion has been produced, a standard ant system updates the pheromone trails asfollows: first all trails are weakened to simulate the evaporation of pheromone;then, pheromone trails that correspond to components that were used to con-struct the resulting solution are reinforced, taking into consideration the qualityof this solution.

Ant systems have been applied to different combinatorial optimization problemslike TSPs [25, 34, 46] and Quadratic Assignment Problems7 (QAP) [34] withcomparable or even better performances than other natural inspired systems,like Genetic Algorithms. For more practical combinatorial problems, like trans-port logistic problems or project and process planning problems these very newoptimization techniques are also in use [33]. Ant systems are used for routing andload balancing in circuit switched telecommunications networks [94] and routingin packet switched telecommunications networks like the Internet [32]. For con-tinuous optimization problems, like the base station parameter optimization ofan wireless network the ant colony technique is not fully developed to achievegood results in agreeable time [57].

7Quadratic Assignment Problem: The problem of assigning a set of facilities to a set oflocations with given distances between the locations and given flows between the facilities. Thegoal then is to place the facilities on locations in such a way that the sum of the productbetween flows and distances is minimal.


Chapter 4

Coverage- and Capacity-limitingFactors

4.1 Introduction

Unlike GSM, the coverage and capacity improvement methods cannot be sepa-rated anymore in the UMTS system. There is always a tradeoff between coverageand capacity. Some of the improvement methods enhance the coverage at the costof capacity, while others improve capacity, but at the same time the coverage de-creases.

In UMTS the network coverage and capacity can be either uplink or downlinklimited. It is generally accepted that service coverage is uplink limited. How-ever, system capacity may be either uplink or downlink limited depending uponthe system configuration and the traffic profile. In rural environments, wherethe network is normally planned with relatively low uplink load, the scenario istypically capacity limited in the uplink. A downlink capacity-limited scenario ismore likely in an urban scenario, where the network is planned for higher uplinkload to increase the system capacity [68].

When a cell’s capacity limitation is reached, additional users cannot be admittedto the system and, therefore, they are put to ”outage”. Outaged users are withinthe coverage of the cell, but not able to access the network services. Thus, as thenumber of users at outage increases, the network capacity decreases. The outageproblems can be managed by radio resource management (RRM) and optimiza-tion of the base station parameters. Therefore, understanding and identifyingthe limitations is important for the development of optimization strategies forincreasing coverage and capacity effectively.

This chapter provides the basis for understanding the reasons for coverage andcapacity limited scenarios both in the up- and downlink. Further, the corre-

49

50 CHAPTER 4. COVERAGE- AND CAPACITY-LIMITING FACTORS

sponding solutions for the enhancement of network coverage and capacity arepresented.

4.2 Uplink and Downlink Coverage-limited Sce-

narios

The majority of existing literature makes the assumption that service coverageis uplink limited [68]. In general this is true, though it is fairly easy to iden-tify scenarios where service coverage is downlink limited, for example when thedata rate is asymmetric with more data in the downlink combined with a limitedbase station transmit power capability. The simplest method for studying ser-vice coverage performance is using a link budget. For the identification, whichparameters need to be improved to enhance service coverage performance, thelink budget is also very useful.

Techniques, which require additional investments for improving the service cov-erage are active antennas, mast head amplifiers, higher-order receive diversity,increased sectorization, repeaters and smart antennas. Some of these techniquesimprove coverage, but at the cost of capacity. However, other techniques likesmart antennas simultaneously improve both coverage and capacity. A detaileddescription of the individual techniques can be found in [68]. A good overviewon smart antennas is given in [16, 45].

Link budgets for a W-CDMA system follow the same principles as those for GSM.The main differences are the inclusion of processing gain, Eb/N0 requirement, softhandover gain, target uplink cell loading and a headroom to accommodate thefast power control loop. In the link budget the target loading is the main capacity-related parameter. A low value for the target loading corresponds to a larger cellrange, but a lower cell capacity.

The link budget for a data service supporting 384 kbit/s on the downlink and64 kbit/s on the uplink is presented in Table 4.1. The lower allowed propagationloss value indicates that the service coverage is uplink limited. If we assume apower of 37 dBm for the power amplifier of the base station, the service coverageof the scenario gets downlink limited. A maximum of half of the total transmitpower is generally allocated to any one single link, i.e. 34 dBm (similar to theexample from Table 4.1 where 40 dBm represents half of the total transmit powercapabiliy of a 43 dBm power amplifier). Therefore, the downlink allowed propa-gation loss decreases by 6 dB and results in a downlink-limited service coverage.

A series of typical uplink link budgets is presented in Table 4.2 for a range ofservice data rates. The difference in the allowed propagation loss value maybe used to estimate the difference in site count requirements for various service

4.2. UPLINK AND DOWNLINK COVERAGE-LIMITED SCENARIOS 51

Parameter Uplink Downlink

Service bit rate 64 384 kbit/s

Maximum transmit power 21.0 40.0a dBm

Antenna gainb 0.0 18.5 dBi

Body loss / cable lossb 0.0c 2.0 dB

Transmit EIRP 21.0 56.5 dBm

Processing gain 17.8 10.0 dB

Required Eb/N0 2.0 4.5 dB

MDC gain 0.0 1.2 dB

Target loading 50 80d %

Rise over thermal noise 3.0 7.5 dB

Thermal noise density -174.0 -174.0 dBm/Hz

Receiver noise figure 3.0 8.0 dB

Interference floore -168.0 -159.0 dBm/Hz

Receiver sensitivity -117.9 -99.9 dBm

Receiver antenna gain 18.5 18.5 dBi

Cable loss/ body lossb 2.0 0.0c dB

Fast fading margin 3.0 0.0 dB

Soft handover gain 2.0 2.0f dB

EIRP required -133.4 -101.9 dBm

Allowed propagation loss 154.4 158.4 dB

Table 4.1: Example link budget for a data service (source: [68]).a 40 dBm is a typical limit placed upon a downlink traffic channel for a 43 dBmpower amplifier module to prevent an excessive share of base station power beingallocated to a single user.b The values for the antenna gain, body loss and cable loss are very optimistic.A detailed research on this topic shows [87].c It has been assumed that data services do not incur a body loss.d The downlink target loading is a function of the traffic mix loading in the cell.80% is a typical value.e Measurements of the background noise floor can be found in [82].f The value for soft handover gain in the downlink is too hard. Typical values,based on numerous simulations can be found in [17].


coverage objectives [68]. Table 4.2 shows that the highest data rate service definesthe cell range in terms of the allowed propagation loss. Planning the networkfor a 384 kbit/s service coverage will be sufficient to ensure acceptable coverageperformance for lower data rate services and speech.

Service type Speech Data Data Data

Uplink bit rate 12.2 64 144 384 kbit/s

Maximum transmit power 21.0 21.0 21.0 21.0 dBm

Antenna gaina 0.0 0.0 2.0b 2.0b dBi

Body loss / cable lossa 3.0 0.0c 0.0c 0.0c dB

Transmit EIRP 18.0 21.0 23.0 23.0 dBm

Processing gain 25.0 17.8 14.3 10.0 dB

Required Eb/N0 4.0 2.0 1.5 1.0 dB

Target loading 50 50 50 50 %

Rise over thermal noise 3.0 3.0 3.0 3.0 dB

Thermal noise density -174.0 -174.0 -174.0 -174.0 dBm/Hz

Receiver noise figure 3.0 3.0 3.0 3.0 dB

Interference floor -168.0 -168.0 -168.0 -168.0 dBm/Hz

Receiver sensitivity -123.1 -117.9 -114.9 -111.1 dBm

Receiver antenna gain 18.5 18.5 18.5 18.5 dBi

Cable loss/ body lossa 2.0 2.0 2.0 2.0 dB

Fast fading margin 3.0 3.0 3.0 3.0 dB

Soft handover gain 2.0 2.0 2.0 2.0 dB

EIRP required -138.6 -133.4 -130.4 -126.6 dBm

Allowed propagation loss 156.6 154.4 153.4 149.6 dB

Table 4.2: Example uplink link budgets for illustrating the impact of service datarate (source: [68]).a The values for the antenna gain, body loss and cable loss are very optimistic.A detailed research on this topic shows [87].b It has been assumed that terminals supporting higher data rates are superior interms of antenna configuration.c It has been assumed that data services do not incur a body loss.

Improving any of the parameters in the link budget will lead to an improvementin service coverage performance. However, improving service coverage leads to agreater average base station transmit power requirement per downlink connection.

4.3. UPLINK CAPACITY-LIMITED SCENARIOS 53

If the system capacity is uplink limited, then this is of no consequence. Although,if the system capacity is downlink limited, then improving service coverage willlead to a loss in system capacity. A different approach would be to improve theEb/N0 performance. Then, it is possible to simultaneously enhance both servicecoverage and system capacity.

4.3 Uplink Capacity-limited Scenarios

In the uplink, there are two possible limiting factors for uplink capacity-limitedsystems. One reason could be that the mobile doesn’t have enough transmitpower to achieve the required bit energy to interference plus noise density ra-tio (Eb/I0) to access the network services. An uplink capacity-limited scenariocan also occur when the maximum uplink load is reached and therefore no addi-tional users can be accepted in the system. The traffic associated with an uplinkcapacity-limited scenario is generally relatively symmetric.

4.3.1 Insufficient Uplink Power

The maximum allowed transmit power of a mobile must be enough to fulfill theEb/I0 requirement at the base station in order to access the network services.

The transmit power PTX,MS needed for the mobile is calculated using Equ. (4.1)and compared to the maximum allowed.

PTX,MS =N0 · Lp

ν · (1− ηUL) · (1 + WR·ρ·ν )

(4.1)

Where N0 is the background noise, Lp is the propagation loss between the mobileand the base station, R, ν and ρ are the bit rate, service activity and uplinkEb/N0 requirement of the chosen service respectively, W is the W-CDMA chiprate and ηUL is the uplink loading.

Hence, if the mobile fails to fulfill the required Eb/I0, the RNC commands themobile to increase its transmit power through the closed loop power control algo-rithm, which is based on the received power measured at the base station. If thisis not possible, because the maximum transmit power of the mobile is achieved,the mobile is put to outage.

From Equ. (4.1) we can see that the required transmit power of a mobile isdirectly proportional to the path loss. Consequently, this power level could bereduced by decreasing the path loss, e.g. by adjusting the antenna downtilt orthe antenna azimuth.


4.3.2 Uplink Cell Load Limitation

An uplink capacity-limited scenario is likely to occur in environments where thecapacity requirements are relatively low and the network has been planned witha low uplink cell load to maximize cell range and thus reduce the requirementsfor the sites. The maximum permissible level of uplink cell load ηUL,threshold de-termines the interference margin that appears in any link budget calculation (seeTable 4.1). The greater the cell loading, the greater the required number of sites,as well as the higher potential capacity per site. The traffic in an uplink capacity-limited scenario is generally relatively symmetric. The uplink load equation isdefined as Equ. (4.2) [68].

ηUL =KN∑k=1

1

1 + Wρk·Rk

· (1 + i) (4.2)

Where KN is the number of mobiles connected to base station N. Rk and ρk

are the bit rate and Eb/N0 requirement from the user k of the chosen service,respectively. W is the W-CDMA chip rate and i is the other-to-own cell receivedpower ratio. This ratio is defined as Equ. (5.1) in Section 5.2.2.

In an uplink capacity-limited system, the capacity per cell is directly proportionalto the maximum permissible level of uplink cell load [68]. Each mobile whichestablishes a connection with the same Eb/N0 requirement and activity factor,increases the cell load by the same amount. Doubling the maximum cell loadηUL,threshold results in doubling the cell capacity for an uplink-limited scenario.The impact upon cell range is dependent upon the absolute levels of the cell load.The relationship between the cell load and the maximum allowed propagation lossis exponential [68]. In Equ. (4.3) the relationship between the uplink cell loadηUL and the resulting increase in receiver interference floor L is shown.

L = 10 · log10(1− ηUL) (4.3)

As ηUL achieves 100%, the receiver interference floor increases without limit.However, this condition in practise can never occur, because the mobiles have afinite transmit power capability.

When the maximum uplink load of a cell is reached, ηUL ≥ ηUL,threshold (whereηUL,threshold is the planned maximum permissible level of uplink cell load), anyadditional users will be set to outage even though the users would have enoughtransmit power to access the network services.

In the following list the reasons, why the maximum uplink cell load is reachedare summarized:

4.4. DOWNLINK CAPACITY-LIMITED SCENARIOS 55

• The network is planned with a too low uplink cell load ηUL.

• High base station transmit power capability.

• Relatively symmetric traffic (e.g. speech users).

For a uplink capacity limited scenario, more users can be admitted into thesystem by shrinking the planned service coverage area of the cells (by the use ofdown tilting the antenna) so that the mobiles at the cell boundaries handover toadjacent cells that have lower traffic density to achieve load balancing within thenetwork. As a result, the overall network capacity will be improved.

4.4 Downlink Capacity-limited Scenarios

A downlink capacity-limited scenario occurs due to several reasons: the maximumtransmit power of the base station is reached, OVSF code utilization reaches itslimitation, or the requested code power for a mobile is higher than the permittedlevel.

Downlink capacity-limited scenarios are likely to occur in suburban or urbanenvironments, where the the network has been planned to a relatively high uplinkcell loading [68]. The traffic associated with a downlink capacity-limited scenariois generally asymmetric, with a greater amount of traffic in the downlink.

4.4.1 Cell Power Limitation

Downlink capacity-limited scenarios due to maximum cell power are likely tooccur where the network has been configured with low base station transmitpower capability, which may have been done in some circumstances to reducethe requirement for power amplifier modules. In Equ. (4.4) the total requiredtransmit power from the serving base station is shown.

PT =n∑

i=1

PTX,n + Pcommon (4.4)

In Equ. (4.4) PTX,n is the required code power for the connected user n, andPcommon is the overall transmit power of the common channels. In general, ap-proximately 20% of the maximum cell power PT,max is assigned to the pilot andcommon control channels. The remaining 80% is available to support trafficchannel capacity.


When the base station reaches its maximum transmit power level, PT = PT,max

(where PT,max is the maximum base station transmit power capability), it cannotallocate extra power to an additional user even if the cell is not highly loaded. Inthis case, additional users cannot be added without modifying the base stationconfiguration.

All active users belonging to a cell, including those mobiles connected by softhandover share the total transmit power PT . Hence, a lower average code powerrequirement P TX (P TX = 1

n

∑ni=1 PTX,n) results in a higher cell capacity. Fur-

thermore, it is possible to increase the number of served users by reducing thesoft handover overhead. Soft handover links only occur at the cell border, whichexperience maximum path loss and therefore require higher code power.

Table 4.3 shows a range of typical W-CDMA base station transmit power con-figurations PT,max. The capacity offered by each transmit power configurationPT,max is a function of the traffic profile as well as the maximum propagation lossdefining the cell range. The greater the propagation loss, the greater the averagecode power P TX and the lower the cell capacity. In other words, we can say thatthe smaller the cell size the lower P TX and the higher the cell capacity.

Base station Tx power Application

per cell per carrier

37 dBm (5W) Provides low capacity for scenarios where the main

objective is providing service coverage.

40 dBm (10W) Provides medium capacity per carrier. Two carriers

each configured with 10W provide greater capacity

than one carrier configured with 20W.

43 dBm (20W) Provides high capacity per carrier.

46 dBm (40W) Provides increased capacity per carrier when

maximum allowed propagation loss is relatively high.

Table 4.3: Typical base station transmit power configurations (source: [68]).

As we can see from Equ. (4.4), a part of the total transmit power of the basestation is assigned to the common pilot channel (CPICH) and the other com-mon channels. Consequently, by reducing the CPICH power and the powers ofthe other common channels, more power will be available to support the trafficchannel capacity.

In the following list the reasons, why the maximum transmit power PT,max of abase station is reached, are summarized:

4.4. DOWNLINK CAPACITY-LIMITED SCENARIOS 57

• The network is planned with a too high uplink cell load ηUL.

• Low base station transmit power capability.

• Greater traffic on the downlink (asymmetric traffic, e.g. data users).

4.4.2 Orthogonal Variable Spreading Factor (OVSF) CodeLimitation

In W-CDMA spreading and scrambling operations are performed in two steps.The user signal is first spread by the channelization code, called orthogonal vari-able spreading factor (OVSF) code, and then randomized by the scrambling code.In the downlink, scrambling codes are used for the separation of the individualcells, which have maximum 512 available codes. The separation of the individualdonwlink connections for different users within one cell is done by the OVSFcodes. A detailed description about the scrambling and spreading operations arepresented in Section 2.2.2.

Channelization codes become the limitimg factor under relatively high through-put scenarios [68]. This is likely to occur in either microcell or indoor scenarios,where the cell range is limited and code orthogonality is high.

One possible solution to avoid OVSF code limitation is to reduce SHO links.Hence, the number of available OVSF codes for serving links increases. This isbecause SHO connections require independent channels from serveral base sta-tions at the same time and therefore are reducing the available OVSF codes.

Another possibility when the channelization codes become the limiting factorwould be to use a second scrambling code to introduce a second channelizationcode tree. The two code trees will not be orthogonal to another and so this willcause higher intra-cell interference.

4.4.3 Code Power Limitation

In the downlink, each dedicated link needs a certain transmit power to reach theEb/N0 requirement for a sufficient connection to the mobile. In Equ. (4.5) therequired transmit power for a certain link is presented.

PTX ≥ρ · R

W∑k

βk

Lpk·(Itot−αk·Ik+NMS)

(4.5)


Where NMS is the background noise level at the mobile station, Itot is the totalwideband interference power received at the mobile station, Ik is the total wide-band power received at the mobile station from base station k, Lpk

is the linkloss from base station k to the mobile station, αk is the orthogonality factor ofcell k and βk is the scaling factor (relative maximum link powers) for differentbase stations in the active set.

This code power PTX is limited per single traffic link. This limitation is definedas the maximum transmitted power PTX,max on one channelization code on agiven carrier. If the code power PTX requested by a mobile is higher then thepermitted level (PTX > PTX,max), the mobile will not be admitted.

4.5 Summary

Understanding the limitation mechanisms for service coverage and system capac-ity forms an essential part of being able to develop effective capacity optimizationstrategies for UMTS radio networks.

Service coverage and system capacity can be either uplink or downlink lim-ited. Coverage is generally uplink limited, although a low base station transmitpower capability combined with asymmetric data services may lead to a down-link coverage-limited scenario. Capacity may be either uplink or downlink limiteddepending upon the planned level of uplink loading, the base station transmitpower capability, the traffic loading of the network and the performance of thenetwork.

There are various available techniques to increase the network capacity. Accord-ing to [68], the simplest and most effective way to increase system capacity isto add one or more carriers. When all the available carriers have been used,then other methods such as additional scrambling codes, mast head amplifiersand active antennas, remote HF head amplifiers, high-order receive diversity,downlink transmit diversity, beamforming, sectorization, repeaters, micro cellsor smart antennas [16, 45] could be applied. In [68] the most of these techniquesare described in more detail.

There are also several possible ways to increase the network capacity withoutadditional infrastructure and cost investment, for instance:

• Minimizing the intra- and inter-cell interference

• Shrinking the service coverage area

• Optimizing the CPICH power allocation

4.5. SUMMARY 59

• Optimizing SHO links and overhead

This thesis focuses on possible ways of increasing the network capacity on sys-tem level optimization corresponding to base station configuration. Hence, theconsidered capacity limited scenarios are:

• Cell load limitation

• Cell power and OVSF code limitation

Uplink cell load, base station transmit power and OVSF code utilization are apowerful measure for radio network optimization.


Chapter 5

Key Optimization Parameters

5.1 Introduction

In this chapter the parameters, which are used in this thesis for optimizing thecapacity in a UMTS FDD network, are described in more detail.

Careful configuration of the many network and cell parameters is required andcrucial to the network operator, because they determine the capability to provideservices, influence the quality of service (QoS), and account for a major portionof the total network deployment and maintenance costs. However, there arenumerous configurable base station parameters which are multi-dimensional andinterdependent, and their influence on the network is highly non-linear.

The following list shows the most important parameters:

• Antenna settings

– Antenna azimuth

– Antenna tilt

– Height

– Antenna pattern

• Primary common pilot channel (P-CPICH) power level

• Soft handover parameters

– Active set size

– Active set window

61

62 CHAPTER 5. KEY OPTIMIZATION PARAMETERS

All these parameters have a strong influence on the interference in the systemand therefore on the amount of served mobile terminals1 (capacity of the net-work). The optimization algorithms described in this thesis focus on optimizingP-CPICH power as well as antenna tilt and antenna azimuth. So, in the followinga description of these three parameters is presented. Furthermore, the influence ofthese key optimization parameters on the network, especially on system capacityand coverage will be explained.

5.2 Antenna Parameters

The antenna parameters are the most important ones related to the interferencesituation in the network. Besides the height of the antenna and the used pattern,the azimuth angle and the elevation angle can be tuned. The height of the antennaas well as the antenna azimuth can only changed with a higher operating expense.However, changing antenna tilt and antenna pattern is associated with less effort.

5.2.1 Antenna Azimuth

This thesis focuses on base stations with 3 sectors (”cells”) and a fixed spacing of120◦ between the three antennas. When adjusting the antenna azimuth, all threeantennas are turned in the same direction at the same time, so that the spacingbetween them will be kept constant at 120◦, as shown in Figure 5.1. The arrowssymbolize the directions of the main beams of the antennas.

Figure 5.1: Adjustment of base station azimuth.

For finding the optimum azimuth settings in a network, the interference has to betaken into account. The goal of the azimuth optimization in this work is to reducethe intra- and inter-cell interference. As a result the capacity of the network willbe increased. In Figure 5.2, the horizontal pattern of the used KATHREIN 739707

1A mobile terminal is defined as served, if it is able to access a network service.

5.2. ANTENNA PARAMETERS 63

antenna [64] is shown. The pattern shows a difference in antenna gain of about6 dB between the main direction of the antenna (0◦) compared to an angle of± 60◦

(at this angle the adjacent sectors of this base station begin, and there the mobilestations will initiate a handover to the neighboring cell). Due to that differenceof 6 dB, the direction of the main beam of the antenna is quite significant andthus it is important to adjust the azimuth of the antennas in order to reach thehighest antenna gain for the users in the own cell, as well as the lowest gain (orhighest attenuation) for the mobile stations located in neighboring cells. Thisway, less power is needed for covering the area, and therefore less interference isgenerated.

Figure 5.2: Horizontal pattern of base station antenna (in dB).

5.2.2 Antenna Tilt

The antenna tilt is defined as the elevation angle of the main beam of the antennarelative to the azimuth plane. Since the tilt is usally set in the direction downto the ground, the term downtilt is often used. A positive downtilt is definedas the negative elevation angle of the main beam of the antenna relative to thehorizontal plane (see Figure 5.3). The service area in Figure 5.3 is the own celland the far-end interference area is the area of the adjacent cells.

The antenna downtilt can be implemented in a mechanical way as well as byelectrical tilting. These two tilting mechanisms have different effect: When usingmechanical tilting, the antenna pattern itself stays constant and is only tilted,


Figure 5.3: Adjustment of base station downtilt.

while with electrical tilting the antenna pattern changes when adjusting the tilt.Due to the complexity of analyzing the system with changing the antenna pat-tern for every tilt value, in this thesis only mechanical tilting is applied. So, theoptimization algorithms in this work are evaluated on networks with one fixedantenna pattern (KATHREIN 739707 antenna [64]) with a fixed predefined elec-trical tilt of 3◦ included in the vertical antenna pattern. Figure 5.4 shows the usedvertical antenna pattern. A very detailed examination on the effect of electricaland mechanical antenna down-tilting in UMTS networks can be found in [44].

Figure 5.4: Vertical pattern of base station antenna (in dB).

Antenna downtilt is often used in mobile wireless systems, particularly in theUMTS network, where traffic in all cells is simultaneously supported using thesame carrier frequency. The desired effect is a reduction of the other-to-own cellinterference ratio i, which is defined according to [68] in Equ. (5.1).

5.2. ANTENNA PARAMETERS 65

i =Ioth

Iown

(5.1)

In Equ. (5.1) Ioth denotes the inter-cell interference (interference from the othercells) and Iown is the intra-cell interference (interference from the own cell).

By down-tilting the antennas, the other-to-own-cell interference ratio i can bereduced: The antenna main beam delivers less power towards the neighboringbase stations, and therefore most of the radiated power goes to the area that isintended to be served by this particular base station [20]. Due to the fact thatthe interference in the system is decreasing, the capacity increases and more userscan be served in the network. However, down-tilting the antenna will also reducethe sectorization efficiency, which will decrease the cell capacity.

Both, other-to-own-cell interference ratio i and sectorization efficiency affect theoverall network capacity. According to [20], for small and moderate antennadowntilt angles, the improvement provided through inter-cell interference rejec-tion dominates, and a net increase in capacity can be achieved. For larger downtiltvalues, the reduction of sectorization efficiency dominates, and the result is a netdecrease in capacity.

Additionally, antenna tilt adjustment also affects the cell coverage area. Toomuch down-tilting causes that the service area could become too small and alsoholes in the coverage of the network can occur. Furthermore, if the down-tiltingreaches a certain value, the interference in the neighboring cells increases againdue to the side lobes of the vertical antenna pattern. Figure 5.5 in Section 5.4shows this effect. In [88] it is shown that, for smaller inter-cell site separation,higher downtilt is required to mitigate the inter-cell interference. As the inter-cellsite separation increases, smaller downtilt is advantageous, offering higher gainsto distant users. Hence, the impact on the cell coverage area limits the tilt toreasonable values.

The simulation analysis of [69] shows that the optimum value for the antenna tilt-ing depends on the propagation environment, the cell site, user locations, and theantenna radiation pattern. Furthermore, in order to achieve the highest numberof served users, it is very crucial to effectively control the inter-cell interferenceand soft handover overhead. It also stated that due to the antenna radiationpattern side lobes and nulls, there could be some variations of i and coverageprobability can occur as a function of the antenna tilting angle.

In [72] it is demonstrated that antenna tilt tuning can also help to relieve conges-tion in hot-spot sectors and maintain the blocking probability at an acceptablelevel.

Detailed descriptions of the effect of antenna tilt on the system capacity arepresented in [20, 60, 69, 72, 88].


5.3 Common Pilot Channel (CPICH) Power

The common pilot channel in UMTS consists of two subchannels, the primaryCPICH (P-CPICH) and the secondary CPICH (S-CPICH). A detailed descriptionof the CPICH channels is given in Section 2.3.2. The algorithms in this thesisfocus on the optimization of the P-CPICH power, and therefore the term CPICHwill be used for P-CPICH in the following.

The CPICH is very important for handover, cell selection and cell reselection. Af-ter turning on the power of the mobile station and while roaming in the network,the mobile measures and reports the received level of chip energy to interferenceplus noise density ratio (Ec/I0) on the CPICH to the base station for the cellselection procedures. Ec is the average energy per pseudo noise (PN) chip, andI0 denotes the total received power density, including signal and interference, asmeasured at the mobile station antenna connector2. This Ec/I0 ratio is given byEqu. (5.2),

Ec

Io

=RSCPCPICH

RSSI(5.2)

where the received signal code power (RSCPCPICH) is the received power of theCPICH measured at the mobile station. It can be used to estimate the path loss,since the transmission power of the CPICH is either known or can be read fromthe system information. The received signal strength indicator (RSSI) is thewideband received power within the relevant channel bandwidth in the downlink.

The cell with the highest received CPICH level at the mobile station is selectedas the serving cell. As a consequence, by adjusting the CPICH power level, thecell load can be balanced between neighboring cells, which reduces the inter-cellinterference, stabilizes network operation and facilitates radio resource manage-ment [103]. Reducing the CPICH power of one cell causes part of the terminalsto hand over to adjacent cells, while increasing it invites more terminals to handover to the own cell, as well as to make their initial access to the network in thatcell.

During the radio network planning process, the CPICH transmit power of thebase stations should be set as low as possible, while ensuring that the serving cellsand neighboring cells can be measured and synchronized to and the CPICH can be

2There exist quantities that are a ratio of energy per chip to PSD. This is the commonpractice of relating energy magnitudes in communication systems. It can be seen that if bothenergy magnitudes in the ratio are divided by time, the ratio is converted from an energy ratioto a power ratio, which is more useful from a measurement point of view. It follows that anenergy per chip of X dBm/3.84 MHz can be expressed as a mean power per chip of X dBm.Similarly, a signal PSD of Y dBm/3.84 MHz can be expressed as a signal power of Y dBm.

5.3. COMMON PILOT CHANNEL (CPICH) POWER 67

used as a phase reference for all other downlink physical channels. Too high valuesof CPICH power will cause the cells to overlap and therefore create interferenceto the neighboring cells, called ’pilot pollution’, which will decrease the networkcapacity. Furthermore, the CPICH power is part of the total transmit powerof the base station, which is generally limited. Thus, less CPICH power wouldprovide more power for the traffic channels, and therefore increase the capacity.On the other hand, the mobile stations are only able to receive the CPICH downto a certain threshold level of Ec/I0, which determines the coverage area. Due tothat fact, setting the CPICH power too low will cause uncovered areas betweenthe cells. In an uncovered area, CPICH power is too weak for the mobile todecode the signal, and call setup is impossible. According to the specifications ofthe Third Generation Partnership Project (3GPP), the mobile must be able todecode the pilot from a signal with Ec/I0 of -20 dB [2].

To make Equ. (5.2) better understandable, the Ec/I0 ratio can also be describedwith Equ. (5.3).

CPICHEc/I0 =

PCPICH

Lp∑numBSsi=1

PTX,i

Lpi+ IACI + N0

(5.3)

Where PCPICH is the CPICH power of the best server, Lp is the link loss to thebest server, PTX,i is the total transmit power of BS i, Lpi is the link loss to basestation i, IACI is adjacent channel interference, N0 is the thermal noise of themobile and numBSs is the number of base stations in the network.

The soft handover (SHO) area can also be controlled by the strength of theCPICH power. By reducing the CPICH power, the SHO areas will decrease.However, a certain amount of overlapping cell boundaries is necessary for mobilesnear the cell border to perform SHO and to counteract fluctuations of receivingsignal power.

As conclusion, the level of the CPICH power is very important to reach highcapacity in the system. Therefore, the CPICH power level is a key optimizationparameter and is included in the optimization strategies for the increase of thecapacity in this thesis. The power levels of the other common channels (PCH,SCH,...) are typically set with respect to the CPICH power level [68]. Therefore,the optimization algorithms in this thesis change the power of the PCH and SCHin the same way like the CPICH power. For example, if the CPICH power isdecreased by 1 dB also the power of PCH and SCH is reduced by 1 dB.

Further information regarding the influence of the adjustment of CPICH poweron the system capacity can be found in [60, 65, 68, 72, 91, 103]


5.4 A Short Investigation of CPICH and An-

tenna Tilt Changing

To show the influence of CPICH power and antenna tilt the different settings forall base stations are evaluated on the small scenario. In Section 7.4.2 this scenariois introduced. It consists of 9 base stations equipped with 3-sector antennas,thus comprising 27 cells. In this scenario only speech users are used insteadof two-way 64 kbit/s data users, which are normally used for the small networkscenario. Figure 7.4 shows the distribution of the base stations as well as the userdistribution.

For this investigation the antenna downtilt was varied from 0◦ up to 16◦, in stepsof 1◦, with the same value for the antenna tilt in each cell. Further, the CPICHpower value was changed from 33 dBm in 1 dB steps down to 10 dBm, again withthe same value in each cell. All possible combinations for these two parameterswere evaluated on the small scenario with the UMTS FDD network simulator inCPICH coverage verification mode 1 (see Section 7.2.2.1). A 3D-plot in Figure 5.5shows the results of this investigation.

Figure 5.5: Capacity for different CPICH power and antenna tilt settings. (Ca-pacity is measured as served users.)

In Figure 5.5 the x- and y-axis show the key optimization parameters CPICHpower and antenna tilt. The z-axis shows the number of served users in the

5.5. SUMMARY 69

network. From the plot we see that if the CPICH power is varied by the samevalue in each cell, there is no strong influence on the number of served users.This means that the absolute level of the CPICH power is not so importantfor the capacity, because the serving cell areas remain the same. However, ifthe cells would have different power values for the CPICH, this will influence theserving cell areas in the network and so causes an increase or decrease in capacity,depending on the setting.

The antenna tilt affects the capacity in a different way. From Figure 5.5 we candivide the antenna tilt range in 4 areas:

• area1 : 0◦ ÷ 6◦

• area2 : 6◦ ÷ 11◦

• area3 : 11◦ ÷ 13◦

• area4 : 13◦ ÷ 16◦

In area1 the capacity increases, if the antenna downtilt value increases. This isdue to the reduction of inter-cell interference. In area2 the capacity decreasesdue to the antenna radiation pattern (see Figure 5.4 in Section 5.2.2). The firstside lobe in the antenna pattern (compare with Figure 5.4) increases the capacityin the network in area3. From a downtilt of 13◦ (area4 ) up to higher values thecapacity decreases again. Note that these results are only valid for a scenariowith a plane terrain. In a scenario with hilly terrain the situation can be totallydifferent.

5.5 Summary

Adjusting the antenna parameters antenna tilt and antenna azimuth as well asthe CPICH power enables to increase the network capacity by:

1. Reducing inter-cell interference and pilot pollution.

2. Optimizing base station transmit power resources.

3. Load sharing and balancing between cells.

4. Optimizing SHO areas.


The amount of transmit power for the CPICH is not specified in the 3GPPstandard. So, it is up to the network operator to assign appropriate power levelsto the CPICH. In [68], it suggests that the CPICH power is set to about 5-10%of the total cell transmit power capability.

Despite the challenges associated with CPICH power, antenna tilt and antennaazimuth settings adjustment, it is clear that these parameters provide low costtechniques for optimizing expensive UMTS networks, because no additional ex-penditure in infrastructure is necessary.

Chapter 6

Fitness Function andPerformance Indicators

6.1 Introduction

In this chapter the performance indicators of the various optimization algorithmsare presented. The indicators are the basis of decisions for the CPICH power andantenna tilt adjustments. The algorithms presented in Section 8.2 and Section 8.3use the number of outaged mobiles as well as the quality factor QF, introducedby SYMENA, Software & Consulting GmbH, for these decisions.

First, the used fitness functions for UMTS network optimization in this work areintroduced in Section 6.2. Section 6.3 describes the definition of the Grade of Ser-vice (GoS), which is an important side constraint for the optimization throughoutthis thesis. The two important performance indicators, the number of outagedmobiles and the quality factor, respectively, will be described in Section 6.4.1and 6.4.2.

6.2 Fitness Function

For the evaluation of the network, a fitness function has to be defined. The fitnessfunction represents the optimization goal. This thesis focuses on the capacityoptimization in a UMTS FDD network. So, the the main goal of the optimizationprocess is to increase the number of served users.

In this section two fitness functions, which are used during this work, are pre-sented. First, a basic version is introduced, which is used for the local optimiza-tion algorithms (Section 8.2). During the development of the genetic algorithm

71

72CHAPTER 6. FITNESS FUNCTION AND PERFORMANCE INDICATORS

(global optimization technique) in Section 8.3, the fitness function was improvedby including the coverage area as well as the soft handover.

6.2.1 Basic Fitness Function

As mentioned before, the goal of the optimization is to increase the capacity. So,the basic fitness function considers the number of served users in the networkas the goal of optimization. Equ. (6.1) shows the fitness function for one testsolution i1.

g(i) =cells∑k=0

servedk (6.1)

In Equ. (6.1), servedk is the number of served users of cell k, and cells is thenumber of total cells in the network.

6.2.2 Extended Fitness Function

The basic fitness function (see Equ. (6.1)) was extended for the use of the geneticalgorithm. As before, the number of served users is used again in the fitnessfunction. In addition to the capacity, also the coverage area and the number ofmobiles in soft handover are taken into account for the fitness calculation. Thisis done to increase the accuracy of the fitness value and to allow the algorithmto differentiate between two otherwise equal solutions: If two solutions have thesame number of served users, the solution with the larger coverage area andfewer mobiles in SHO has a higher fitness value. In Equ. (6.2) the extendedfitness function g(i) for one test solution i is shown.

g(i) =cells∑k=0

servedk + gcov + gSHO (6.2)

In Equ. (6.2), servedk is the number of served users of cell k, and cells is thenumber of total cells in the network. The term gcov represents the coverage prob-ability of the pixels in the simulation area (covered pixels over existing pixels),scaled between 0 (no coverage) and 1 (all pixels are covered). The SHO propor-tion is taken into account by the term gSHO in Equ. (6.2). In order to serve moremobiles in a cell, the idea is to reduce the number of SHO links in this cell. InEqu. (6.3) the calculation of gSHO is shown.

1In this thesis one test solution means one evaluated network state with one particularparameter setting (antenna azimuth, antenna tilt and CPICH power).

6.2. FITNESS FUNCTION 73

gSHO = 1− 1

cells

cells∑k=0

gSHO,cell(k)

gSHO,max

(6.3)

The term gSHO,cell(k) in Equ. (6.3) denotes the number of SHO links in cell kand gSHO,max is the maximum over all gSHO,cell(k). The range of gSHO is between0 and 1. A high value of gSHO means a low SHO rate and a low value of gSHO

characterizes a high amount of SHO connections.

There is always a tradeoff between capacity and coverage, QoS and costs. Thegoal in this thesis is to increase network capacity in a cost effective way, whilemaintaining the coverage and QoS. Figure 6.1 shows the tradeoff between thesefactors.

Figure 6.1: The three tradeoffs of UMTS optimization (source: [75]).

6.2.3 Used Fitness Function

Several optimization algorithms are presented in this thesis, using either thebasic fitness function presented in Section 6.2.1 or the fitness function from Sec-tion 6.2.2. Table 6.1 summarizes which fitness function is used for which algo-rithm.


Algorithm Section Used fitness function

Rule Based Approach 8.2.1 g(i) =cells∑k=0

servedk

Simulated Annealing 8.2.2 g(i) =cells∑k=0

servedk

Adaptive Rule Based Approach 8.2.3 g(i) =cells∑k=0

servedk

Genetic Algorithm 8.3 g(i) =cells∑k=0

servedk + gcov + gSHO

Analytic Optimization Algorithm 8.4 g(i) =cells∑k=0

servedk†

†The fitness function value is not considered by the Analytic Optimization Algorithm, but itwas used during the development of the algorithm. Further details are described in Section 8.4.

Table 6.1: Assignment fitness function - optimization algorithm.

6.3 Grade of Service

Besides the fitness value, a second indicator is very important during the opti-mization process, which is used to characterize the proportion of the users thatcan be provided with a service. This value is called Grade of Service (GoS) anddescribes the ratio of served users over all existing users2. In this thesis the GoSis defined as

GoS =served

existing(6.4)

In Equ. (6.4), served denotes the total number of served users in a defined area(e.g. the whole simulation area), and existing is the total number of simulatedusers in the same area. During the optimization process, GoS increases from itsinitial value of 95% until it has reached 100%. Then all users are served and theoptimization algorithm cannot proceed any further. However, the network couldaccept more users. Thus, the different optimization algorithms developed in histhesis apply the following approach: When GoS reaches a value of 96%, newusers are added to the network until the initially defined GoS of 95% is reachedagain.

The calculation of the number of additional users is done within the networkevaluation with the UMTS FDD network simulator described in Section 7.2. The

2Note that some literature defines GoS as the probability of a call being blocked or delayedfor more than a specified interval.

6.4. PERFORMANCE INDICATORS 75

service mix (40% speech users and 60% 64 kbit/s data users) after adding the ad-ditional users remains the same. For the big network scenario (see Section 7.4.1)the new users are only added in the optimization area (usergroups optarea1 andoptarea2 ), and not in the whole network scenario. However, in the small scenariothe additional users are added in the whole network. Remember, the optimizationarea in the small scenario covers the whole network scenario (see Section 7.4.2).In this thesis, the function for adding the additional users is referred to as AddUsers.

6.4 Performance Indicators

6.4.1 Outaged Mobiles

The first performance indicator that is used for the algorithms are the outagedusers of a cell. A user is put to outage due to several reasons. These reasons arecalled the outaged reasons. In Chapter 4, a general description of the coverage-and capacity-limiting factors, which reflect the outage reasons, is given.

The evaluation of a network scenario with the static UMTS FDD network sim-ulator delivers the number of outaged mobiles per cell. The following list showsthe used outaged reasons of the simulator:

• DL outage:

– DL cell power: If the total base station power is to high, the connec-tion with the highest code power is closed. This is repeated until thebase station transmit power is below the predefined maximum value.The outage priority could also be on the existing SHO connections(not implemented in the used simulator). The latter criterion usuallyequals the former, since SHO connections in most cases have to betransmitted with higher power levels than active links. This is ob-vious, since SHO connections only occur at the cell border and theyrequire higher DL code power levels as active connections.

– DL code power: If the required code power for a mobile is to high, thismobile is put to outage.

– OVSF code power limitation: In that case the number of connectionshas to be reduced. Similar to the procedure regarding the maximumbase station power, the priority is on the connection with the highestpower, but could also be on the SHO links.


– Ec/I0 requirement: The mobiles, which do not fulfill the Ec/I0 require-ments are put to outage. (Only in CPICH coverage verification mode2, see Section 7.2.2.2)

• UL outage:

– UL power: If the required transmit power of the mobile exceeds themaximum transmit power, the connection will be closed.

– UL load: If the actual load exceeds the planning threshold (also called”system design load”), the user with the highest transmit power willbe put to outage. This procedure is repeated until the system load isbelow the predefined maximum system load.

6.4.2 Quality Factor

To describe the quality of a cell in the network, a performance indicator denotedas quality factor (QF ) is introduced. The QF indicates whether a cell is heavilyloaded or not, and it is the minimum of the following three values:

1. cell load factor

2. cell pwr factor

3. ovsf factor

The cell load factor is a measure of the uplink cell loading condition and isdefined as

cell load factor =load threshold− cell load

load threshold(6.5)

In Equ. (6.5), load threshold denotes the planned uplink cell load and cell loadis the actual cell loading. The second value, cell pwr factor, is a measure of basestation transmit power utilization in the downlink and is defined as

cell pwr factor =max cell pwr − cell tx pwr

max cell pwr(6.6)

In Equ. (6.6), max cell pwr denotes the maximum transmit power of a cell andcell tx pwr represents the current total transmit power of that cell. The thirdvalue, ovsf factor, is a measure of the OVSF code utilization in the downlinkand is defined as

6.4. PERFORMANCE INDICATORS 77

ovsf factor =ovsf limit− ovsf utilization

ovsf limit(6.7)

In Equ. (6.7), ovsf limit is the maximum available number of OVSF codes, whichis 512; ovsf utilization is the number of used OVSF codes. For example, a voicecall occupies 2 codes, so 256 voice users can be served. If there are only 64 kbit/sdata users 32 users can be served, because one 64 kbit/s data user occupies 16codes.

The range of each of these three factors is between zero and one. For the QFthe minimum of these three values is taken and therefore the range of the QFis between zero and one. A low value for QF describes a heavily loaded cell,and a high value for QF describes a weakly loaded cell. Therefore, by usingthe QF as the performance indicator, CPICH power and antenna tilt settingscan be adjusted adaptively according to the loading condition in the uplink anddownlink of a cell.

6.4.3 Summary of Performance Indicators

In this chapter two performance indicators, the number of outaged mobiles as wellas the quality factor QF were introduced. Nor for all algorithms, both indicatorsare used. Table 6.2 gives an overview which indicator is used for which algorithm.

Algorithm Outaged mobiles Quality factor QF

Rule Based Approach√

Simulated Annealing√

Adaptive Rule Based Approach√

Genetic Algorithm√ √

Analytic Optimization Algorithm†

†The analytic optimization algorithm uses different parameters as basis for the adjustment ofthe parameters, which are described in Section 8.4.

Table 6.2: Used performance indicators for the several algorithms.


Chapter 7

Simulation Environment

7.1 Introduction

For the evaluation of the different optimization techniques, the algorithms haveto be evaluated on a network scenario. Furthermore, a network simulator isnecessary to calculate the coverage and capacity relevant information.

In this chapter the used static UMTS network simulator and the interactionwith the optimization algorithms are described in Section 7.2 and Section 7.3.Further, the network scenarios, on which the different algorithms are evaluated,are presented in Section 7.4.

7.2 The Used UMTS FDD Simulator

For the evaluation of the network configuration in this thesis, a static UMTSFDD network simulator based on the Monte Carlo approach is used. This ap-proach utilizes a sufficient number of independent snapshots of potential userdistributions with one fixed network configuration. With this they allow thecompilations of significant statistics of the system performance parameters. Inthe static approach, each single snapshot corresponds to a network situation inequilibrium, which means that the physical layer procedures like power controlare applied iteratively for each user in the system. With this we can combat thenear-far problem of the real system and find a stable system condition as it wouldbe, if we have fast power control in real time. Due to the static scenario there isno need for a real time fast power control implementation.

The Monte Carlo approach takes not only propagation conditions into account,but also the changes of the individual services, data rates, requirements, user

79

80 CHAPTER 7. SIMULATION ENVIRONMENT

positions and some other time-invariant parameters like SHO-gain. In the sim-plest case the different snapshots (realizations) in a run represent various userpositions in the network area. For each realization (which are initialized at thebeginning of each run) an iteration is done until a certain convergence criterionis fulfilled. This criterion can, as an example, be based on the variation of theTX power per iteration for all mobiles in the system. If the change request forthe transmit power of a terminal between two consecutive iterations is below acertain threshold for, e.g. 95% of all mobiles, convergence is attained. When thisstable situation has been reached, the number of mobiles able to achieve theirperformance targets can be determined and a new realization can be computed.For a representative statistical evaluation it is important to perform sufficientrealizations of the investigated configuration. The principles of the Monte Carloapproach as well as a schematic flowchart of a static simulator are shown inFigure 7.1.

Figure 7.1: Schematic principle of a static Monte Carlo simulator.

Examples for the UMTS system simulations using the Monte Carlo method canbe found in [30, 58, 62, 68, 81]. Proposals for the number of snapshots that haveto be done are given in [5].

The simulator used in this work is provided by SYMENA, Software & Consulting

7.2. THE USED UMTS FDD SIMULATOR 81

GmbH, Austria (http://www.symena.com). Coverage, capacity, and quality ofservice related issues can be analyzed with this tool. Input to the simulator is thenetwork configuration and the user distribution. Uplink and downlink are jointlyanalyzed, and the simulation results comprise coverage and capacity information,traffic statistic, outage reasons for the unserved users, and soft handover (SHO)statistics.

7.2.1 Parameters of the Network Simulator

For the network simulator a lot of different parameters have to be defined. Inthe following the values of the main parameters are introduced. Appendix Isummarizes all the network parameters.

The simulator uses one carrier and one OVSF code tree. The maximum power ofall cells in the network is set to 43 dBm, the maximum code power to 40 dBm andthe minimum to 15 dBm. The P-CPICH power level (CPICH) is set to 33 dBm(10% of the maximum cell power) in the reference scenario (initial value). Addi-tionally, there are parameters for SCH and PCH power, which are both 5 dBm.However, these two parameters are only included in the interference calculationand have no other impact. Note that during the optimization process for theCPICH power, SCH and PCH are changed in the same way (see Section 5.3).The active set window is 3 dBm and the active set size is set to 2. The receiverof the base station utilize 3 rake fingers and use a maximum ratio combiningefficiency for uplink softer handover operations of 100%. Appendix C presentsa detailed description of the rake receiver. The transmitter loss is 0 dB and thebase station noise figure 5 dB. Furthermore, there is the possibility to set thetransmission power control (TPC) dynamic range and headroom of the downlinkas well as for the uplink, which are 25 dB and 1 dB for the downlink as well as65 dB and 2 dB for the uplink.

In the network conventional antennas are used, with a height of 30m in all cells.The base stations have 3 sectors using antenna type 739707 from Kathrein withan antenna gain of 16 dBi [64]. The horizontal and vertical antenna pattern areshown in Figure 5.2 and Figure 5.4. The vertical antenna pattern presents anelectrical tilt of 3◦. For the optimization of the antenna tilt only the mechanicaltilt is changed. The initial mechanical tilt is set to 0◦ in the reference scenario.

As parameters of the channel, the following values can be specified in the simu-lator: The background noise floor is set to -107 dBm, and as path loss model animproved COST 259 model for macro cells is used. A detailed description of theCOST 259 model can be found in Appendix B. The large scale fading has zeromean and a standard deviation of 2 dBm. The maximum delay of the channel is2µs and the total number of multipath components of the channel is set to 5.


The inter-symbol interference of the channel destroys the orthogonality of thecodes. This is described by the downlink orthogonality factor, which is 0 if thecodes are perfectly orthogonal, and 1 if the codes are totally non-orthogonal. Thedownlink orthogonality factor is chosen to be 0.4.

The standard deviation of the gaussian distribution of the azimuth angles of thechannel taps (sigma azimuth of taps) is set to 5◦. The adjacent cell interferenceratio (ACIR) is 5% and the inter system interference ratio is 5% as well. Thesetwo parameters only describe an increase of the existing interference level in thesystem due to other carries or other systems like GSM.

Further information about the several network parameter can be found in [68].

7.2.2 CPICH Coverage Verification Modes

For the optimization of a UMTS system the CPICH power level is very important(see Section 5.3 and Section 5.4). The level should be optimized very accuratelyto use the power resources of the base station efficiently, to reduce the interferencelevel, and nevertheless provide the required CPICH coverage in the system.

Due to the importance of the CPICH coverage and to achieve the possible mini-mum of CPICH power level, it is mandatory to know how the simulator definesCPICH coverage when deriving CPICH optimization strategies.

The UMTS network simulator CAPESSOTM from SYMENA provides two modesfor CPICH coverage verification: mode 1 with a fixed threshold for CPICH cov-erage and mode 2, including the interference by a Ec/I0 threshold (for details see[68]). In the following both modes are explained in detail.

7.2.2.1 Mode 1

In the CPICH coverage verification mode 1, the CPICH coverage is defined byusing the received power of the CPICH measured at the mobile station comparedto a certain threshold for the lower limit, the receiver sensitivity. Expressed inEqu. (7.1) it is:

RSCPCPICH ≥ S (7.1)

In Equ. (7.1) RSCPCPICH is the received signal code power of CPICH and S isthe receiver sensitivity with a default value of -120 dBm.

It is crucial to mention that the CPICH coverage verification mode 1 does not takeany interference into account for the calculation of coverage. In this mode theCPICH coverage of a single cell would be the same with or without the presenceof the total system.

7.3. SIMULATOR INTERFACE 83

7.2.2.2 Mode 2

The CPICH coverage verification mode 2 includes the interference level of the sys-tem in the calculation of CPICH coverage. In this mode, a mobile in the simulatedscenario is covered by the CPICH if the EC/I0 for the received CPICH at the mo-bile station exceeds a certain threshold. This threshold is called CPICH EC/I0 thresaccording to the 3GPP specification [2] and is set to -12 dB for the most of thesimulations performed in this work. The mobiles, which don’t fulfill the Ec/I0

requirements are put to outage (see Section 6.4.1). In CAPESSOTM the Ec/I0

threshold is set by the receiver sensitivity parameter. A receiver sensitivity of-120 dBm is equivalent to an EC/I0 threshold of -12 dB. Equ. (7.2) shows therelationship between EC/I0 threshold and the receiver sensitivity.

CPICH EC/I0 thres = S − Pnoise (7.2)

In Equ. (7.2) S denotes the receiver sensitivity and Pnoise is the noise power at20◦C, which is -108.09 dBm.

Also, the signal to noise ratio (SNR) is called CPICH EC/I0 although there is animportant difference, which will be explained in the following. In Equ. (7.3) thecalculation in CAPESSOTM for mode 2 is presented.

CPICHEc/I0 =RSCPCPICH

RSSI −RSCPCPICH

≥ CPICH Ec/I0 thres (7.3)

In Equ. (7.3) RSCPCPICH denotes the received signal code power of the CPICHas measured by the mobile and RSSI (received signal strength indicator) is thewideband received power within the relevant channel bandwidth in the downlink.

The mentioned difference is that in the 3GPP specification the CPICHEc/I0 isthe ratio of the received energy per PN chip for the CPICH to the total receivedpower spectral density at the UE antenna connector as shown in Equ. (5.2) andEqu. (5.3), whereas in CAPESSOTM the CPICHEc/I0 is the real SNR.

7.3 Simulator Interface

The static UMTS network simulator CAPESSOTM from SYMENA, Software &Consulting GmbH consists of two parts: the simulation engine and the graphicaluser interface (GUI). The GUI helps the operator to feed the simulation with therelevant data (user distribution, network configuration and parameters). Further,the GUI prepares the simulation results in tables, diagrams and plots for the


operator. As interface between the simulation engine and the GUI, XML1 filesare used, as shown in Figure 7.2.

Figure 7.2: Interface between network simulator and optimization algorithm.

Two files are used as interface: One, which includes all the input informationfor the simulator, in Figure 7.2 named as XML Input File. The second one, theXML Output File, is generated after the simulation is finished and comprises allthe results for the GUI.

The XML files are also used as interface for the optimization process. The opti-mization algorithm prepare the input file for the simulator and starts the simu-lation. After the simulation is finished the output file of the network analysis isread from the optimization algorithm for calculation of the fitness function andGoS. Fig 7.2 shows this interface marked in blue.

7.4 Network Scenarios

For the evaluation of the several optimization algorithms two virtual networkscenarios of a typical European city are used. In the first scenario (bigger one)the network covers the whole area of the city. The second scenario contains onlydowntown. In the following both scenarios are described. The more interestingone is the bigger one. For the results present in Chapter 9 normally this one isused. Only if it is explicitly mentioned the small scenario is used.

1XML: Extensible markup language. The next-generation of HTML, is now viewed as thestandard way information will be exchanged that don’t share common platforms (www.xml.org).

7.4. NETWORK SCENARIOS 85

7.4.1 Big Scenario

In the big network scenario 25 base stations equipped with 3-sector antennas,thus comprising 75 cells, are used. Figure 7.3 shows the distribution of the basestations as well as the distribution of the users. The arrows in the figure representthe main antenna direction of each cell, and the black dots symbolize the usersin the system.

Figure 7.3: Base station location and one user distribution of the big networkscenario.

In this simulation scenario the area inside the rectangle is defined as the opti-mization area. This means that this region is the area of interest, and the fitnessfunction as well as the GoS is evaluated over this part.

In the total simulation area the users are distributed equally in each cell accordingto the best server plot. The best server plot shows the regions of the dominanceareas of the different cells due to the highest received CPICH power level. Thatmeans, all the pixels (one pixel is equivalent to 100 m× 100 m) in the best serverplot of the scenario with the highest received CPICH power level from one cell


(so-called serving cell) are dedicated to that cell and describe the best server areaof that certain cell. The same number of mobiles are distributed in each of theseareas. This kind of distribution is called ”Best Server Equal” distribution in thisthesis. It is also possible to use an equally distributed user model. In this modelthe users are equally distributed over a defined rectangular area. In this workthis distribution model is called ”Equal” distribution.

For the simulation all the mobiles are assigned to four different user groups. It ispossible to use ”Best Server Equal”and ”Equal”distribution in each user group,as well as a certain service (voice or data). The big scenario uses four usergroups:usergroup1, usergroup2, optarea1 and optarea2. The first two usergroups coverthe whole scenario and so define the service mix in the whole scenario. Usergroupsoptarea1 and optarea2 only cover the optimization area in the network and sodefine the service mix in this area. For the results present in Chapter 9 normallythe ”Best Server Equal”distribution is used in all usergroups. In the scenario aservice mix of 40% 12.2 kbit/s speech users and 60% two-way 64 kbit/s datausers is assumed with an activity factor of 50% when using speech service and100% when using PS data service. The initial number of users in the wholenetwork is 1057. Additional users during the optimization process are admittedin optarea1 and optarea2 by the Add Users function (see Section 6.3).

For the variation of the positions of the mobiles in the scenario (generate differentsnapshots, see Figure 7.1 in Section 7.2), the initialization value of the randomgenerator for the different usergroups can be changed by a parameter, which iscalled rand init.

In the simulator also some parameters of the mobile stations can be defined andaccordingly varied. The maximum transmit power of the mobile station is setto 21 dBm. The mobiles have an antenna gain of 0 dB, and the body loss aswell as the receiver noise figure are set to 0 dB. The threshold down to whichthe received CPICH power level is considered for the interference calculations is-126 dBm, and the receiver sensitivity S of the mobiles is -120 dBm. Since thereceiver sensitivity defines the CPICH coverage, S can also reach other values(see Section 7.2.2).

7.4.2 Small Scenario

The second scenario, which is used for some investigations in this work representsthe downtown of the European city used for the big network scenario. Thisscenario is equivalent to the optimization area of the scenario described in theformer section.

In the small network scenario 9 base stations equipped with 3-sector antennas,thus comprising 27 cells, are used. Figure 7.4 shows the distribution of the base

7.4. NETWORK SCENARIOS 87

stations as well as one distribution of the users.

Figure 7.4: Base station location and one user distribution of the small networkscenario.

In this scenario the optimization area comprise the whole network. Two user-groups are used: usergroup1 and optarea1. These usergroups cover the wholearea. The ”Best Server Equal”distribution with only two-way 64 kbit/s datausers is used in both usergroups. The initial number of users in the network is376. The other parameters are the same like in the other scenario. Note thatadditional users are only admitted in optarea1 by the Add Users function (seeSection 6.3).


Chapter 8

Optimization Algorithms

8.1 Introduction

In this main chapter of the thesis the developed optimization algorithms are pre-sented. Altogether five different strategies for the optimization of antenna tilt(see Section 5.2.2) and CPICH power (see Section 5.3) were developed. First,the optimization with local algorithms was studied and consequentially a RuleBased Approach algorithm, a Simulated Annealing algorithm and an AdpativeRule Based Approach algorithm originated. Further, a Genetic Algorithm ap-proach was implemented and studied in more detail. The disadvantage of all thelocal and especially the genetic approach is that they are very time consuming.So, also an analytic algorithm was developed with the objective to use as fewsteps as possible in contrast to the other strategies. This algorithm optimizesalso antenna azimuth besides antenna tilt and CPICH power (see Section 5.2.1).

The following list summarizes all developed approaches:

• Local Optimization Algorithms:

– Rule Based Approach

– Simulated Annealing

– Adaptive Rule Based Approach

• Genetic Algorithm

• Analytic Optimization Algorithm

The Adaptive Rule Based Approach and the Analytic Optimization Algorithmwere developed and studied within the scope of the diploma these of Yee Yang

89

90 CHAPTER 8. OPTIMIZATION ALGORITHMS

Chong and Wolfgang Karner under supervision of the author. In this PhD thesisonly a brief summary of the approaches as well as some optimization results forcomparison with the other algorithms are given. For a detailed description aswell as further simulation results see [23, 63].

8.2 Local Optimization Algorithms

In the case of the local algorithms, the optimization of the base station parametersCPICH power and antenna tilt begins with an initial evaluation of the network.After analyzing the results of the first evaluation, the iterative optimization pro-cess is started. Each optimization loop includes two steps. After changing theparameters in the first step, the network is evaluated in the second step. Then,the next iteration of the optimization loop is started and the parameters arechanged again. The optimization runs until a specific termination condition isfulfilled. Figure 8.1 shows the flow chart of the optimization process.

Figure 8.1: Structure of local optimization process.

For the three different local approaches this optimization process is always thesame. The difference lies in the block Change parameters. Further, the decision,which results (test solutions) are accepted and which are not is different for thestrategies. In the three Sections 8.2.1, 8.2.2 and 8.2.3 the individual approachesare explained and the differences are worked out.

8.2. LOCAL OPTIMIZATION ALGORITHMS 91

8.2.1 Rule Based Approach

The algorithm is a rule based optimization approach. It starts from a scenariowhere all cells of the network are set to the identical initial values for CPICHpower and tilt. After a first evaluation, the optimization starts by reducing theCPICH power and increasing the antenna downtilt in individual cells accordingto a configurable rule set. After each optimization step, a new evaluation ofthe fitness function is perfomed. The algorithm of the Rule Based Approach isillustrated in Figure 8.2, which reflects the optimization process from Figure 8.1.Break denotes the termination condition.

Procedure RuleBased()begin

X := initial parametersfit := evaluation(X)while != break

X ′ := change parameters(X)fit′:= evaluation(X’)if (fit′ > fit)

X := X ′

fit := fit′

endend

end

Figure 8.2: Rule Based Optimization Algorithm.

The crucial part of the algorithm is the function change paramters(x) (see Fig-ure 8.2). This function processes a rule set like the one depicted in Table 8.1 andis executed for each cell with outaged mobiles (see Section 6.4.1). The parame-ters of the table describe the following: param specifies the modified parameter;delta denotes the amount of change; limit describes the lower or upper limit ofthe parameter, and iter specifies how often the rule is applied at most. Whenthe optimization process is launched, the algorithm starts with the first rule ofthe rule set. In each iteration only one rule is applied. According to Table 8.1,we can see that after the first 5 optimization loops the algorithm continues withrule number 2 and so on. During one rule also worse results are accepted. Whenadvancing to the next rule, however, the best result of the previous one is taken.The algorithm terminates, when all rules of the rule set have been processed. InAppendix D the standard rule set, which is used in Chapter 9, is presented.


rule param delta limit iter

1 CPICH -5 dB 28 dBm 5

2 tilt 5◦ 5◦ 5


4 tilt 3◦ 8◦ 10


6 tilt 1◦ 10◦ 10

Table 8.1: Example rule set for Rule Based Optimization Algorithm.

8.2.2 Simulated Annealing

Subsequently the Rule Based Approach was extended and improved by incorpo-rating Simulated Annealing [9]. The decision to accept a bad result is in contrastto the Rule Based Approach independent of the rule set. After an optimizationloop worse results can be taken, but only with a certain probability. This prob-ability corresponds to a cooling function (CF). The exponential CF is analogousto the physical process of heating and then slowly cooling steel to obtain a crys-talline structure with minimum entropy. In the beginning of the optimization theprobability to take a bad result is higher. During the optimization the probabilityto accept a worse result than the previous one decreases according to the CF.Section 3.3 gives an detailed overview about Simulated Annealing.

Concerning the implementation, the main difference to the Rule Based Approachis shown as bold text in Figure 8.3. The variable rand denotes a uniformlydistributed random value between 0 and 1. CF is the cooling function of theSimulated Annealing algorithm. In this algorithm the same decisions for changingCPICH power and antenna tilt settings are used. So, a rule is executed in eachcell with outaged mobiles. However, the number of outaged mobiles as well asthe outage reason do not influence the decision.

The same type of rule set (see Table 8.1) as in the Rule Based Approach isused for Simulated Annealing in the function change paramters(x). For the Sim-ulated Annealing algorithm the rule set was improved by including additionalrules with smaller values for delta. Appendix E presents two developed rule sets(see Table E.1 and Table E.2), which were applied for the optimization results inChapter 9. Again, the basic fitness function from Equ. (6.1) is used.

The implementation of Simulated Annealing in this thesis uses only the rule set forfinding a new parameter setting and not the Simulated Annealing mechanism. So,like in the Rule Based Approach it is only possible to decrease the CPICH power


Procedure RuleBased()begin

X := initial parametersfit := evaluation(X)while != break

X ′ := change parameters(X)fit′:= evaluation(X’)if (fit′ > fit) or (rand < CF )

X := X ′

fit := fit′

endend

end

Figure 8.3: Extension of Rule Based Optimization algorithm to Simulated An-nealing.

and increase the antenna downtilt. The idea was to use Simulated Annealing foraccepting also bad results with a certain probability.

In the algorithm two different functions for the cooling temperature TC are im-plemented. On the one hand Geometric Cooling according to [9] is implementedwith the following function:

TC,NEW = TC · α (8.1)

In Equ. (8.1) α denotes a parameter of the function. On the other hand also SlowCooling is implemented according the following function:

TC,NEW =TC

1 + β · TC

(8.2)

In Equ. (8.2) β denotes a parameter of the function. In Chapter 9 both coolingfunctions for the cooling temperature TC are used for optimization. For thecooling function the following equation is used:

CF = eGoS−GoSOLD

TC (8.3)

In Equ. (8.3), GoS denotes the actual GoS and GoSOLD represents the GoS ofthe previous iteration. On the one hand, CF depends on the cooling temperatureTC . On the other hand, it also depends on the change of the GoS. Thus, for


big differences in GoS (if the result is worse than before), the value of CF, andthus the probability to take the new result is lower. If the random value rand issmaller than the CF the optimization result is taken (see Figure 8.3).

If a result is not accepted, the previous result is modified again according thesame rule. However, only in half of the cells the parameter (CPICH power orantenna tilt) is changed. For this, the cells are sorted by the number of outagedmobiles with the most outaged mobiles first. If in the last iteration only in onecell the parameter was changed, then the algorithm processes with the next ruleand changes the parameter again in all cells with outaged mobiles.

8.2.3 Adaptive Rule Based Approach

In this section the developed Adaptive Rule Based Approach is described. Thisoptimization algorithm is an extension of the Rule Based Approach introducedin Section 8.2.1.

The parameters are changed according to an adaptive optimization technique,described in the following. Figure 8.1 shows the basic optimization process. Thisalgorithm differs from the previous two described algorithms (Section 8.2.1 andSection 8.2.2) that now CPICH power and antenna tilt are changed together.Further, an increase of CPICH power and antenna uptilting is also possible.Remember, the Rule Based Approach and Simulated Annealing were only ableto change the parameters in one direction (reduce CPICH power and reduceantenna downtilt).

8.2.3.1 Why Adjust CPICH Power and Antenna Tilt Together?

In this section the idea, why CPICH power and antenna tilt are adjusted together,is explained considering an example. Figure 8.4 shows this example. In the firstpicture (Figure 8.4 (a)) two mobiles are shown. Both are served by base stationBS 1. The goal is to achieve a load balancing in a way that one mobile is servedby BS 1 and the other by BS 2. This aim can be reached with three differentstrategies.

The first strategy decreases the CPICH power of BS 1. Figure 8.4 (b) shows thissituation. Now, each base station serves one mobile, but with the disadvantagethat BS 1 causes inter-cell interference to the cell of BS 2.

In the second strategy the antenna downtilt of BS 1 is increased to shrink thecoverage area of the cell (Figure 8.4 (c)). However, the CPICH power is to highfor the smaller cell and so affects pilot pollution to the adjacent cell. Further, the


Figure 8.4: Why adjust CPICH power and antenna tilt together?


excessive CPICH power causes a waste of base station power resources, becausethe total transmit power is limited.

The last strategy, which is the basis for the Adaptive Rule Based Approach,combines the previous ones and changes CPICH power and antenna tilt together.So, no additional inter-cell interference and pilot pollution is produced like instrategy one and two. This combined strategy is shown in Figure 8.4 (d).

8.2.3.2 Algorithm Description

In each iteration of the optimization loop (see Figure 8.1) the quality factor QF(see Section 6.4.2) is computed for each cell. CPICH power and antenna tilt arechanged according to the developed rules, as shown in Table 8.2.

QF CPICH power and antenna tilt change

QF < 0.5 Decrease CPICH power and tilt antenna down

0.5 ≤ QF ≤ 0.7 No change

QF > 0.7 Increase CPICH power and tilt antenna up

Table 8.2: CPICH power and antenna tilt adjustment.

The rules are designed in such a way that a highly loaded cell (QF < 0.5)having outage users is required to shrink its coverage area by decreasing theCPICH power and increasing the antenna downtilt. Conversely, if the cell has alow user density (QF > 0.7), it has to expand its coverage area to cover moreusers by increasing the CPICH power and antenna uptilt 1. With this approach,load balancing within the cells in a network can be achieved, resulting in highernetwork capacity.

CPICH power and antenna tilt modifications are controlled by a set of rules withdifferent stepsize and limitation settings, as shown in Table 8.3. In principle thesame type of rule set is used as for the Rule Based Approach, however withthe important difference that now one rule consists of two instructions: oneinstruction for the CPICH power and one instruction for the antenna tilt.

In Table 8.3 param specifies the modified parameter; stepsize denotes the maxi-mum allowed adjustment of CPICH power and antenna tilt settings per iteration;limit describes the lower or upper limit of the parameter, and iter specifies howoften the rule can be applied at most.

1Numerous simulations with different values for the limits of QF from Table 8.2 show that0.5 and 0.7 achieves the best results.


rule param stepsize limit iter

1 CPICH 3dB 25 dBm 50

tilt 1.5◦ 4◦

2 CPICH 0.5 dB 10 dBm 50

tilt 0.25◦ 7◦

Table 8.3: Example of CPICH power and antenna tilt limitation & stepsize set-tings.

When the QF of a cell is greater than 0.7, both its CPICH power and antennaup tilt will be increased by

stepsize ·∣∣∣∣QF − 0.7

0.3

∣∣∣∣ . (8.4)

On the other hand, if the cell’s QF is less than 0.5, the CPICH power will bereduced and antenna down tilt will be increased by

stepsize · (1−QF ). (8.5)

Consequently, the adjustments of CPICH power and antenna tilt depend on thecell’s actual QF . With this strategy, CPICH power and antenna tilt are adjustedadaptively according to the loading condition of a cell.

The modification of antenna down tilt and CPICH power has to be limited (lowerlimit for CPICH power and upper limit for antenna down tilt) in each rule bythe parameter limit in order to avoid too big changes in a particular cell (seeTable 8.3). There are no limitations set for the maximum CPICH power andmaximum antenna up tilt, so that larger service coverage area is allowed in regionswhere the user density is low. Furthermore, the developed algorithm is biasedtoward reducing the initial CPICH power and increasing the antenna down tilt.

When the optimization process is launched, the algorithm starts with the firstrule of the used rule set (e.g. from Table 8.3). For each rule several iterations areperformed. According to Table 8.3, we can see that in this case the algorithmcontinues with rule number 2 after the first 50 iterations.

If the GoS after one iteration within one rule is lower than 95 percent and lowerthan the GoS from the previous iteration, the new result is not accepted. In thiscase, the same iteration is processed again, but only in two third of the previouslymodified cells (priority according QF ) the CPICH power and tilt settings are


changed (Cell Reduction). The algorithm terminates, when either all rules of therule set have been processed, or if the QF in all the cells is between 0.5 and 0.7(see Table 8.2). This means, the network is balanced or the algorithm cannotprocess further, due to the limits for CPICH power and tilt. A detailed flowchartof the optimization loop is shown in Figure 8.5.

Figure 8.5: Detailed flowchart of Adaptive Rule Based Approach.

The flowchart in Figure 8.5 shows that after the network evaluation with CAPESSOTM

the GoS is used as fitness function. The goal of the optimization is to maximizethe capacity of the network, and so the number of served users has to be con-

8.3. GENETIC ALGORITHM 99

sidered, like in the basic fitness function in Equ. 6.1. Since the GoS (Equ. 6.4)includes the number of served users in the enumerator, also this performanceindicator is utilizable as fitness function.

Appendix F presents four developed rule sets for the Adaptive Rule Based Ap-proach (see Table F.1, Table F.2, Table F.3 and Table F.4), which were appliedfor the optimization results in Chapter 9.

8.3 Genetic Algorithm

After the development of different local optimization algorithms also a globalapproach was implemented. The theoretical basics of global optimization are de-scribed in Section 3.5. The most popular representative of this family of optimiza-tion techniques are Genetic Algorithms. Section 3.5.1 gives a general overview ofthis technique. In this section, my developed genetic approach, which is especiallyadapted for the optimization of network parameters for the UMTS system, is de-scribed. For the algorithm I use a deterministic fitness proportional selection,a problem specific recombination operator (altogether 3 different versions weredeveloped) and an improved mutation operator. In addition, a simple local opti-mization based on the Rule Based Approach from Section 8.2.1 is implemented,which is applied to the best individuals to improve their fitness. Finally, a paral-lel implementation is introduced to distribute the individual network evaluationsof the population on several computers with a master/slave concept.

For the implementation of the Genetic Algorithm an XML file is used as initial-ization file for the specification of the most important parameters. Appendix Gshows this file.

8.3.1 Representation

For the Genetic Algorithm a suitable representation of the parameters CPICHpower and antenna tilt is needed. In Figure 8.6 the used coding is shown.

Figure 8.6: Representation of individuals for capacity optimization.

Each individual of the population consists of 2n genes, where n is the number ofcells. For one cell, one gene is used for the CPICH power and one for the antenna


tilt. The search space for the algorithm is set in the following way: The range forthe CPICH values is from 15 to 38 dBm with a resolution of 0.5 dB; The valuesfor the antenna downtilt are limited by −2◦ and 10◦. The resolution of the tilt isset to 0.5◦. The element <limit> specifies the search space in the initializationfile (Appendix G).

8.3.2 Algorithm

In this subsection the optimization process as well as the used genetic operatorsare described in more detail. Newly developed operators are used to incorporateknowledge about the quality of the cells in the network. In Figure 8.7, theflowchart of the implemented algorithm is shown.

The algorithm starts with the initialization of all individuals of the population.Section 8.3.2.1 describes this initialization of the population. After the initialphase, the whole population is evaluated with the static UMTS FDD networksimulator. The fitness function g(i) as well the GoS are calculated for all theindividuals (i = 0, 1, ..., n)2. For the fitness evaluation the extended fitness func-tion from Equ. 6.2 in Section 6.2.2 is used. In the next step the GoS of the bestindividual, i.e. the individual with the highest fitness value, is compared to thelimit3 of 96%. If the GoS is higher than this threshold, additional users are addedin the simulation (Add Users)4, and the whole population has to be reevaluatedagain to get the new values for g(i) and GoS.

After this preprocessing of the population, the optimization process is started. Inmy genetic approach I have implemented selection, recombination and mutation.With the selection operator, the individuals for the new population are selected.Of some individuals multiple copies can be selected, and some other individu-als will be not selected at all, according to the implemented selection method.After the selection process, the individuals (parents) are recombined to createnew individuals (children). The last genetic operator randomly mutates genesof the individuals with a certain probability. When the evolution process of onepopulation is finished, the whole population has to be evaluated again to get thenew values for g(i) and GoS of the individuals.

With the best individuals of the population a local optimization step is performedto improve the performance of the Genetic Algorithm. After the local step theGoS of the best individual is compared to the threshold of 96%. If the GoS ishigher, then additional users are added and the population is reevaluated. In

2The variable n denotes the size of the population.3This threshold is specified in the initialization file by the attribute add gos in the element

<ga> (Appendix G).4See Section 6.3 for details.


Figure 8.7: Flowchart of Genetic Algorithm approach.


the next iteration, the evolution process is repeated. The Genetic Algorithmstops, if a certain termination condition is fulfilled, e.g. after a certain numberof iterations.

8.3.2.1 Initial Population

For all the individuals of the population the initial values for CPICH power andantenna tilt are chosen randomly, but with the same CPICH power and antennatilt values for all cells. The values for the CPICH power are chosen within 15and 38 dBm with a resolution of 1 dB. The initial values for the antenna tilt areselected within −2◦ and 10◦ with a resolution of 1◦.

However, not all individuals are initialized randomly. The first 12 individualsof the population are set to fixed predefined values. The element <init> ofthe initialization file (Appendix G) defines the setting. For each of these 12individuals one value for CPICH power and one value for the antenna tilt isdefined. So, each cell of the network has the same CPICH power and antenna tiltvalue. The background of this initialization is that the search space is coveredby the start population as good as possible.

8.3.2.2 Selection

Deterministic Fitness Proportional Selection

The selection of the individuals for the new population is implemented as a de-terministic fitness proportional selection, because trials of several other meth-ods (roulette wheel selection, tournament selection,...)5 have indicated that thismethod fits best for the problem of UMTS base station parameter optimization.The fitness values g(i) of the individuals are scaled to get suitable fitness val-ues f(i) for the selection process. I use a linear scaling function, presented inEqu. (8.6).

f(i) = a · g(i)− b (8.6)

with a =Cmg − g

gmax − gand b = a · g − g (8.7)

5In [76, 77, 92] all the selection methods are explained in detail.


In Equ. (8.7), Cm denotes the selection pressure6, which describes how much thealgorithm favors good individuals compared to bad individuals. The mean valueover all g(i) is denoted as g in Equ. (8.7), and gmax is the highest fitness thatoccurs in the population.

My implementation of the fitness proportional selection works as follows: First,the best individual since the last function call of Add User is selected to guar-antee that the best solution cannot get lost. This method is called elitism7 inliterature [76, 92]. Next, the expected number of descendants for each individualis calculated with the following equation:

e(i) =f(i)∑n

j=1 f(j)· n (8.8)

In Equ. (8.8), n denotes the size of the population. From each individual be(i)c8descendants are produced. To complete the population, the best individual ofthe last population is repeatedly selected, until n individuals are in the newpopulation.

Tournament Selection

As mentioned before, also other selection methods were tested. For the sake ofcompleteness also tournament selection is explained in the following. With thisselection method also good results were achieved during the algorithm develop-ment process.

First, the implemented tournament selection method selects the best individual ofthe old population (elitism). Afterwards, k randomly chosen (equal distributed)individuals are selected. The individual with the highest fitness value of thisselected sub-population is taken over in the new population. This selection pro-cedure is repeated until n individuals are admitted in the new population. Thefactor k controls the selection pressure. A higher value of k corresponds to a highselection pressure and vice versa. The value for k depends on the optimizationproblem. In the case of the considered problem in this thesis, it has been shownthat good values for k lies between 2 and 10% of the population size.

6The selection pressure is specified in the initialization file by the attribute cm in the element<ga> (Appendix G). Good values of Cm for the base station parameter optimization problemare between 3.5 and 6.

7Elitism: Independent of the selection function the best k individuals are taken into the newpopulation to guarantee a monotonic increase of the fitness.

8The b·c operator denotes the floor function.


8.3.2.3 Recombination

Altogether 3 different recombination operators were developed. These 3 operatorsare called:

• Recombinate outage

• Recombinate basic

• Recombinate average

In the following all 3 recombination operators are described.

Recombinate outage

My first implemented recombination operator takes the quality of the cells intoaccount. To describe the quality of a cell the quality factor (QF ), introduced inSection 6.4.2, is used as performance indicator.

The algorithm selects n times randomly two individuals (referred to as parent 1and parent 2 ) from the population and produces a new child with a recombinationprobability pc. The recombination operator is shown in Figure 8.8.

Figure 8.8: Recombination operator.

For each cell in the network the number of outaged mobiles and the QF betweenthe two individuals are compared. If the number of outaged mobiles of parent 2is smaller than that of parent 1 and the QF of parent 2 is better for this cell,then the corresponding genes for this cell are taken from parent 2 for the newchild, otherwise the two genes for CPICH power and antenna tilt are taken fromparent 1. If the algorithm decides not to produce a child by recombination (with


a probability of (1−pc)), then the first selected parent is taken unchanged. AfterRecombinate outage, the population again has a size of n individuals, consistingonly of the children produced by the recombination.

During the development of the Genetic Algorithm this recombination operatorshowed the best performance. So, for the results, which are presented in Chap-ter 9, only the Recombinate outage operator is used.

Recombinate basic

In contrast to the previous recombination operator, the Recombinate basic op-erator selects n/2 times randomly two individuals from the population (parent 1and parent 2 ) and produces two new children (referred to as child 1 and child 2 )with a recombination probability pc.

First, parent 1 bequeaths all his genes to child 1 and parent 2 bequeaths all hisgenes to child 2. So, child 1 and child 2 have the same parameter setting forCPICH power and tilt like parent 1 and parent 2, respectively. Then, for eachcell in the network the number of outaged mobiles and the QF between the twochildren are compared. If the number of outaged mobiles of child 2 is smaller thanthat of child 1 and the QF of child 2 is better for this cell, then the correspondinggenes for this cell are exchanged between the children, otherwise the genes arenot exchanged.

If the algorithm decides not to produce children by recombination (with a prob-ability of (1− pc)), then the selected parents are taken unchanged in the popula-tion. After Recombinate basic, the population again has a size of n individuals,consisting only of the children produced by the recombination.

Recombinate average

The last version of the recombination operator doesn’t take any performanceindicator into account. Hence, this operator performs worst of all 3 procedures.For the sake of completeness also this recombination version is explained in thisthesis.

The Recombinate average algorithm selects n times randomly two individuals(referred to as parent 1 and parent 2 ) from the population and produces a newchild with a recombination probability pc. For each cell in the network the averageof the parent’s CPICH power and antenna tilt setting is calculated and taken asnew setting for the new child. Equ. 8.9 and Equ. 8.10 show the calculation of theaverage values.


CPICHchild =CPICHparent 1 + CPICHparent 2

2(8.9)

tiltchild =tiltparent 1 + tiltparent 2

2(8.10)

If the algorithm decides not to produce a child by recombination (with a proba-bility of (1 − pc)), then the first selected parent is taken over unchanged in thenew population. After Recombinate average, the population again has a size ofn individuals.

8.3.2.4 Mutation

The mutation operator is performed with each individual of the population. Thealgorithm decides for each gene of the individual with a mutation probability pm

whether the value of the gene will be mutated or not (for CPICH power andantenna tilt separately). In Figure 8.9, the mutation for one cell is shown.

Figure 8.9: Mutation operator.

Parameters are changed from their old value xold to their new value xnew accordingto the following rule:

xold −∆x ≤ xnew ≤ xold + ∆x (8.11)

The value for ∆x in Equ. (8.11) is set to 0.5 for CPICH power and antennatilt, respectively. From this interval the value xnew is randomly chosen with aresolution of 0.5. So, three solutions are possible: xnew = xold −∆x, xnew = xold

or xnew = xold +∆x. If xnew lies outside the search space, then the correspondinglimiting value is taken.


8.3.2.5 Local Optimization

After the evolution process, a local optimization with the best local−num in-dividuals is carried out. The flowchart of the local optimization is shown inFigure 8.10.

First, the best local−num individuals are selected. For each of these individuals,local−iter local optimization iterations are performed. These parameters arespecified in the initialization file by the attributes local num and local iter

in the element <ga> (Appendix G). Each iteration includes two steps. In thefirst step, the parameters are changed according to the quality of the cells in thenetwork. The rules for changing the parameters are based on the Rule BasedApproach from Section 8.2.1, with the extension that the parameters can bechanged in both directions. In the second step, the individuals are evaluated. Ifthe fitness value for the new parameter setting of the individual is better thanthe old one, then this setting is taken, otherwise the old parameter setting isretained.

Figure 8.10: Detailed flowchart of local optimization.

I use two rules for the local optimization, the first rule (rule 1 ) to shrink a cell inthe network and the second rule (rule 2 ) to enlarge a cell. In each local iterationfor each individual, a random value decides, whether rule 1 or 2 is used. If rule 1


is selected, then for each cell is checked, if there are outaged mobiles and if QF isbad (QF < 0.1). If this is the case, the CPICH power is decreased by 0.5 dB andthe antenna downtilt is increased by 0.5◦ in this cell, both with a probability of0.7. In the case of rule 2, in cells without outaged mobiles and with a good QF(QF > 0.1), the CPICH power is increased by 0.5 dB and the antenna downtiltis decreased by 0.5◦, both with a probability of 0.5.

8.3.2.6 Adding Users as New Impulse

During the optimization process with the Genetic Algorithm sometimes for sev-eral generations no better results are found. The mathematical meaning of thisis that the algorithm gets stuck in a local optimum. This especially occurs nearthe end of the optimization. So, to counteract this phenomenon, the idea is togive the algorithm a new impulse to find a better result.

This new impulse is given by admitting new users by the Add Users function.Normally, the function is called, if the GoS of the best solution in the populationreaches a value of 96%. Then, so many additional users are admitted to thesystem that the best solution has approximately a GoS of 95%. In the initial-ization file it is possible to configure the algorithm in that way that before theGoS reaches a value of 96% the Add Users function will be called. The attributeadd ever of the element <ga> (Appendix G) specifies the number of generations,after that at the latest the function is called. This means, if add ever is set toa value of 20, at the latest after 20 generations new users area admitted to thesystem. Also, if the GoS of the best individual of the population is lower than96%.

With this additional impulse the algorithm is led in a new direction and showsaltogether better results.

8.3.2.7 Reduced Population Size

A second idea to give a new impulse during the optimization with the GeneticAlgorithm is to increase the selection pressure Cm after a certain number ofpopulations. This can be done either by increasing the value of Cm, or by reducingthe size of the population.

The idea of this strategy is that first the algorithm searches in a broader viewand also tracks solutions with a not so good fitness value. After the selectionpressure is increased, the algorithm focuses more on the better solutions and soincreases the convergence behavior of the algorithm.

In my algorithm I implemented the following approach: In the initialization filetwo attributes (reduce iter and min pop size) of the element <ga> configure


the increase of the selection pressure (Appendix G). After reduce iter gener-ations the size of the population is halved. This means that the population issorted by the individual’s fitness value, and only the first half is consider fur-thermore. The lower limit of individuals in one population is specified by theattribute min pop size. So, the selection pressure Cm is increased9 after eachreduce iter generations. This means that for example, if the Genetic Algorithmstarts with a population size of 200 individuals and reduce iter is set to 100,after 100 generations the size of the population is 100, after 200 generations thereare only 50 individuals in the population and so on. If the size of the populationreaches the lower limit min pop size, then number of individuals aren’t reducedfurthermore.

Simulations have shown that this approach only brings a very small increase ofthe performance of the Genetic Algorithm. So, this feature is not implementedfor the simulation results presented in Chapter 9.

8.3.2.8 Implementation for Distributed Computing

Genetic Algorithms are well suited for a parallel implementation. The big ad-vantage of a parallel implementation is that several computers share the workand so save computation time. If we look at the flow chart of the genetic ap-proach (see Figure 8.7), we see that in the block Evaluation for each individualone network evaluation is performed. This means that altogether n times thesimulation engine (see Figure 7.2 in Section 7.3) will be started. Normally, thenetwork evaluations are done in serial processing on one computer.

For Genetic Algorithms different methods are known for an implementation withseveral processors. The following list gives an overview of the methods. A de-scription of the individual approaches can be found in [79, 92].

• Synchrony master/slave model : A master is responsible for population man-agement, selection, recombination, mutation, etc. For the evaluation of thefitness function, the new individuals are sent to several slaves. When allslaves have done their work, the master starts with the next generation.

Disadvantages:

– Master has to wait until all processors have finished their networkevaluations

– Total breakdown, if master has a malfunction

– High communication effort

9Note, that a reduction of 50 % of the individuals does not mean an increase in the selectionpressure of 50 %.


• Semi-synchrony master/slave model : Works like a steady state algorithm10.The master feeds a slave with a new individual promptly after finishing anevaluation.

• Asynchrony master/slave model : Each processor has its own Genetic Algo-rithm, but they use one population together.

• Parallel island model : Such algorithms assume that several sub-populationsevolve in parallel. The model includes a concept of migration (movement ofan individual from one sub-population to another) and recombination be-tween individuals from different sub-populations. This concept is discussedin detail in [21, 51].

In my Genetic Algorithm I use a synchrony master/slave model for a parallelimplementation. This concept has some disadvantages and other concepts aremuch better. However, it is used because a server/client program with this con-cept was provided by SYMENA, Software & Consulting GmbH. Figure 8.11 showsthe topology of the system.

Figure 8.11: Distributed Computing.

On one processor the Genetic Algorithm is running together with a DistributedClient Scheduler. This scheduler is responsible for the communication with theclients (slaves). The Genetic Algorithm prepares the n XML input files (see

10A steady state GA produces only one new individual per generation and this new individualreplaces one of the current population.


Section 7.3) for the individuals of the current population and initiates the sched-uler to distribute the files to the clients. Figure 8.12 shows a screen-shot of theDistributed Client Scheduler.

Figure 8.12: Distributed Client Scheduler.

The clients are running on other processors in the background. It is possible toconnect up to 8 processors at the same time with the scheduler. When a clientreceives an input file, it starts the network evaluation and when it is finished, theXML output file is send back to the scheduler. Figure 8.13 shows a screen-shotof a client.

Figure 8.13: Client.


So, the clients evaluate the whole population. After finishing the evaluation ofthe last individual the Distributed Client Scheduler finishes its job with preparingall the XML output files for the Genetic Algorithm.

With this approach a speedup in execution time can be achieved with a minimumof 3 clients. If only 2 clients are used, the TCP/IP overhead of the network com-pensates the advantage of more processors. If 8 clients are used at the same timea speedup of about 600% is achieved. This is very important for the evaluation ofbig network scenarios, where the evaluation of one single parameter setting takesa long time. Note, that this parallel implemention only speeds up the runtimeof the algorithm, but it doesn’t influence the process of the optimization nor theresult.

8.3.3 Summary of Genetic Algorithm

During the development process several settings were tried for the parameters ofthe Genetic Algorithm. Table 8.4 shows the best setting. With this setting thehighest increase was achieved for both scenarios (see Section 7.4).

I use a population size of 400 individuals with a selection pressure Cm of 5. Withthis high value for Cm, I facilitate the production of so-called super-individuals toshorten the runtime of the algorithm, thus accepting a reduced solution diversityin the population. The algorithm stops after 350 iterations. This is equivalent to∼ 150 000 network evaluations. On a Pentium IV with a clock rate of 2.0GHz and512MB main memory, my Genetic Algorithm requires about 64 hours runtimewithout distributed computing. If the parallel implementation with 8 clients isused, the same algorithm takes only about 10 hours runtime.

Normally, the settings from Table 8.4 are used for the simulation results presentedin Chapter 9. In cases, where a different setting is used for the algorithm, thealternative setting is explicitly stated.

8.4 Analytic Optimization Algorithm

In this section ’ad hoc’ strategies for the adjustment of the three key optimizationparameters antenna azimuth, antenna downtilt and CPICH power are described,which were developed during a diploma thesis by Wolfgang Karner. ’Ad hoc’in this context means to reach the result only by considering the structure ofthe UMTS network, for example the position of the base stations to each other,the height of the antennas, the height profile of the terrain, or the maximumtransmit power of the base stations. The objective of these strategies is to use asfew steps as possible in contrast to the step by step optimization strategies from

8.4. ANALYTIC OPTIMIZATION ALGORITHM 113

Population size n = 400

Selection pressure Cm = 5

Probability Recombination pc = 0.8

Probability Mutation pm = 0.1

Local Optimization:

Number of individuals local−num = 20

Number of iterations local−iter = 2

Area of search space:

CPICH power 15 dBm÷ 38 dBm

Antenna downtilt −2◦ ÷ 10◦

Resolution 0.5 dBm and 0.5◦

Termination condition after 350 iterations

Table 8.4: Best setting of optimization parameters for GA.

Section 8.2 and Section 8.3, therefore needing as few network evaluations withCAPESSOTM as possible.

The following sections give a brief description of the developed strategies for thethree parameters. The full description is presented in [63].

8.4.1 Azimuth Adjustment Strategy

The azimuth adjustment strategy is based on studying the optimum solution forthe regular hexagonal layout. The optimum azimuth setting for the regular casewas discussed in several papers (e.g. [80]). Figure 8.14 shows the best and theworst case for a scenario with 19 base stations equipped with 3-sector antennas.According to [80], an improper direction of sector antennas in a regular hexagonallayout can cause a capacity degradation exceeding even 20% and requires anincrease of base station transmit power in the range from 3 to 6 dB.

The effect of these directions in a regular hexagonal grid can be seen in Fig-ure 8.15a for the worst case and in Figure 8.15b for the best case, where thepathgain is presented in an area of 5000 times 5000 meters with 19 base sta-tions equipped with 3-sector antennas. The plots in Figure 8.15 are produced bya downlink static UMTS FDD simulator, described in [16]. If the transmittedCPICH power level is added to the pathgain in the diagram, the received CPICHpower level at each point of the area can be obtained. In the worst case scenario


Figure 8.14: Best and worst case of antenna directions in a regular hexagonalgrid. Source: [80].

(Figure 8.15a), there are zones in the network with bad coverage. These criticalzones would need more transmit power to be covered and therefore would causemore interference in the system. In the best case (Figure 8.15b), there are nosuch holes of bad coverage, and the total area is covered more regularly.

a. b.

Figure 8.15: Worst case and best case of base station azimuth in a regular grid,pathgain in dB.


8.4.1.1 Algorithm Description

The azimuth adjustment strategy is based on the knowledge of the regular case.The initial setting for the azimuth values can be arbitrarily chosen. Before opti-mization, all the base stations are marked as unchanged. The algorithm consistsof two steps. First, the main lobes of a partition of the antennas are turned toso-called critical spots. In the second step the remaining antennas are interleaved,so that the interference in the network will be minimized.

Step 1: Turn Base Stations to Critical Spots

For a given UMTS network, first the number and coordinates of the critical spotshave to be defined, as well as the number of base stations nr bs, which should beturned to these critical spots.

For the azimuth adjustment routine one or more critical spots have to be defined.We define a critical spot as a place in the network with a higher minimum distanceto all the base stations than all other places; or in other words: a critical spot isa place with a big distance to all the surrounding base stations. Therefore, it isdifficult to cover that area and to serve mobile stations located there.

For each critical spot the nearest nr bs are determined. The algorithm thenadjusts the azimuth values of these nearest base stations, so that one of the mainlobes of the three antennas points directly to that critical spot. The routinedetermines the rotating angles in a way, that the base stations have to be turnedbetween 0◦ and 120◦ clockwise starting from north (0◦). After a base stationazimuth has been adjusted, this site is marked as changed and cannot be adjustedany more. Figure H.1 in Appendix H shows the flowchart of this step.

Step 2: Interleaving of the Remaining Base Stations

In the second step, the remaining base stations are interleaved. During the az-imuth adjustment there are two types of base stations: changed and unchangedones. For each unchanged base station the distances to all already adjusted basestations are calculated. The base station with the minimum distance to an al-ready changed base station is interleaved first and will be called bs to interleavein the following.

In the next step the rotation angle has to be calculated. For this purpose, the clos-est 5 already adjusted base stations (in the following denoted as bs reference(i), i =1..5) are taken into account. However, a base station is only taken as bs reference(i),if it has a distance to bs to interleave smaller than 1.5 times the distance of theclosest base station. Consequently, the maximum value of i can be smaller than 5.


The rotation angles, by which bs to interleave has to be turned from 0◦ (north)in order to be interleaved with each bs reference(i), are calculated. These an-gles αi are calculated in a way, that the main lobe of one of the three antennasof bs to interleave points directly to bs reference(i). If bs reference(i) looksdirectly to bs to interleave however, the angle is calculated so that no mainbeam of an antenna of bs to interleave points directly back to bs reference(i).Bs reference(i) is defined as directly looking to bs to interleave, if bs to interleaveis within an angle of±30◦ around the main lobe of one antenna of bs reference(i),as can be seen in Figure 8.16.

Figure 8.16: Check, if bs reference(i) looks to bs to interleave.

Now we have up to 5 possible angles for turning bs to interleave. If there isonly one base station as reference, it is trivial. Then we only have one angleα1, and thus we can directly take that angle for bs to interleave. If there ismore than one reference base stations (bs reference(i)) however, the angle foradjusting bs to interleave is calculated by using a sliding window over all theangle values αi. The sliding window size was selected to be 20◦, so that themaximum deviation, resulting from calculating the adjusting angle, does notexceed 10◦. The adjusting angle is the mean of the angles αi in the window. Dueto the period of the angles of 120◦, the center of the sliding window runs from10◦ to 130◦ to close the calculation of the period. Of course, the angles between0◦ and 20◦ have to be copied to the equivalent locations between 120◦ and 140◦

first.

Figure 8.17: Window 1 contains the most angles.


To calculate the rotation angle for bs to interleave, we choose the window whichcontains the most angles (Figure 8.17). If the number of angles is equal in allwindows, we take the window with the minimum difference between the anglescontained in it and calculate the mean angle. If there is only one angle in everywindow, the angle of the nearest base station is taken as the value for turningbs to interleave. After the azimuth value of base station bs to interleave hasbeen adjusted, it is marked as changed.

The algorithm repeats step 2 of the azimuth adjustment routine until no un-changed base stations remain. Figure H.2 in Appendix H shows the flowchart ofstep 2.

8.4.2 Strategy for Antenna Downtilt

The optimum adjustment of the antenna downtilt would be to achieve maximumantenna gain for the mobile stations in the own cell and at the same time havingmaximum loss in the far end interference area that means in the neighboringcells. A trade off has be found between the requirements of the own users andthe advantages for the neighboring cells.

In the analytic optimization strategy the antenna downtilt is adjusted accordingto the mean elevation angle11 of the users in the coverage area of the cell inreference to the base station antenna, as shown in Figure 8.18.

Figure 8.18: Elevation angle of the cell area.

The antenna downtilt is not set directly equal to the mean elevation, but ac-cording to certain rules obtained from results of the Rule Based Approach fromSection 8.2.1. For CPICH coverage verification mode 1 these rules are presented

11The mean elevation angle is defined as the mean value over all elevation angles of the mobilesin one cell. Single mobiles, which are covered in the cell but are situated e.g. just below theantenna or in a remote area of coverage, having very big or small elevation angles and thereforewould falsify the result of the mean elevation, are not considered for the calculation.


in Table 8.5 and in Figure 8.19 as a linearized function over the mean elevationangle. For CPICH coverage verification mode 2 these rules are shown in Table 8.6and Figure 8.20.

Mean elevation angle Antenna downtilt

φ ≤ -1.5 -4

−1.5 < φ ≤ −1 -3

−1 < φ ≤ −0.5 -2

−0.5 < φ ≤ 0 -1

0 < φ ≤ 0.5 0

0.5 < φ ≤ 1 1

1 < φ ≤ 1.5 2

1.5 < φ ≤ 2 3

2 < φ ≤ 2.5 4

2.5 < φ ≤ 3 5

3 < φ ≤ 4 6

4 < φ ≤ 5 7

5 < φ ≤ 6 8

6 < φ ≤ 7 9

Table 8.5: Rules for adjusting the antenna downtilt according to the mean ele-vation angle in CPICH coverage verification mode 1.

These rules are only valid for the antenna pattern of the used KATHREIN 739707antenna [64]. For a different antenna the rules will be different. Note, that thedifference between CPICH coverage verification mode 1 and mode 2 is exactly2◦.

Figure H.3 in Appendix H shows the flowchart for the implementation of theantenna tilt adjustment.

8.4.3 CPICH Power Level Adjustment Strategy

In Chapter 5.3 it has been shown that the optimum CPICH power level is thelowest CPICH value that can be received correctly by the mobile in the servingarea. With this minimum power level, too much overlapping in the CPICHcoverage areas is avoided and therefore the minimum pilot pollution and the


Figure 8.19: Function for adjustment of the antenna downtilt according to themean elevation angle in CPICH coverage verification mode 1.

Mean elevation angle Antenna downtilt

φ ≤ -1.5 -6

−1.5 < φ ≤ −1 -5

−1 < φ ≤ −0.5 -4

−0.5 < φ ≤ 0 -3

0 < φ ≤ 0.5 -2

0.5 < φ ≤ 1 -1

1 < φ ≤ 1.5 0

1.5 < φ ≤ 2 1

2 < φ ≤ 2.5 2

2.5 < φ ≤ 3 3

3 < φ ≤ 4 4

4 < φ ≤ 5 5

5 < φ ≤ 6 6

6 < φ ≤ 7 7

Table 8.6: Rules for adjusting the antenna downtilt according to the mean ele-vation angle in CPICH coverage verification mode 2.


Figure 8.20: Function for adjustment of the antenna downtilt according to themean elevation angle in CPICH coverage verification mode 2.

minimum interference is created in the system. Furthermore, due to the transmitpower limitation of the base stations, it is also an advantage to have the minimumCPICH level, in order to provide more power resources to the traffic channels andtherefore achieve a higher capacity.

The same result can be seen in Figure 8.21, where the total transmit power ofone cell in the optimization area of the big network scenario (see Figure 7.3 inSection 7.4.1) is presented over the CPICH power level. This curve is a result ofsimulations with CAPESSOTM in CPICH coverage verification mode 1. Dur-ing the simulation the CPICH power levels of all cells in the scenario have beendecreased. To achieve a valid result, the cell had 19 mobiles served for all mea-surement points. What we can see is that by decreasing the CPICH power levelof the cells, the total transmit power of the cells decreases as well, not only dueto the amount of reduced CPICH power, but also because of a certain downswinging effect due to a reduced interference level in the system.

Thus, the main task in optimizing the CPICH power level is to avoid a too highdegree of cell overlapping while still having a certain minimum CPICH powerfor providing the required CPICH coverage in the defined area. Therefore, theoverall problem is to find that minimum CPICH level for maintaining CPICHcoverage. Due to the significance of the correct calculation of CPICH coverageand because of the quite different coverage verification mode 1 and mode 2, twostrategies for the two modes have been developed.


Figure 8.21: Transmit power of one cell in the optimization area of the big networkscenario.

8.4.3.1 Strategy for CPICH Coverage Verification Mode 1

In CPICH coverage verification mode 1, the coverage is defined by the mobile’sreceiver sensitivity, but without consideration of any interference. Therefore, therequired coverage can be maintained down to a non realistic CPICH power levelof less than 0 dBm at a receiver sensitivity of -120 dBm, as set in the simulator. Tohave a more realistic lower bound of CPICH power level, a minimum of 15 dBmwas set for the CPICH in the optimization strategy.

Starting from that minimum CPICH power level of 15 dBm, it is quite easy toadjust the CPICH power levels in all cells for the required CPICH coverage inthe defined area, since no interference is included in the coverage verification.What we have to do is to look at all the pixels in the defined area, which arenot covered and adjust the CPICH power level of the cell with the smallest pathloss to that pixel. The amount of CPICH power, which has to be changed in thebase station is the difference of the received CPICH power level at the mobile tothe threshold of -120 dBm. In this way we try to cover a predefined part of thesystem area. One UMTS network operator for example could aim for a coverageof 98% in urban and 80% in rural areas.

In the XML output file of the simulator no direct information about the receivedCPICH power levels at every pixel of the scenario is available, so a overcrowdedscenario with about 100 users per cell is used. This is no problem in CPICHcoverage verification mode 1, as for coverage verification the interference is notconsidered. These 100 mobile stations per cell, which are measuring the receivedCPICH level, are almost covering the whole cell area and therefore provide a


good statistics for the CPICH levels in the cell.

8.4.3.2 Strategy for CPICH Coverage Verification Mode 2

In CPICH coverage verification mode 2 the CPICH coverage is defined by acertain CPICHEc/I0 threshold. In this mode the simulator considers the inter-ference level in the system for the coverage verification. Due to the fact thatwhen changing the CPICH power levels, the interference is changing as well, it isvery difficult or impossible to adjust the minimum required CPICH power levelin one step.

The worst case for coverage is the case with the highest possible interference levelin the system which happens when all the base stations are transmitting with themaximum power. At the same time this is the ideal case for capacity, becausethe system will accept users until the power limit is reached if there is no othercapacity limitation factor reached before (see Chapter 4 for capacity limitationfactors).

Therefore, we can conclude that the CPICH coverage should always be adjustedaccording to the worst case, which is the highest possible interference level in thesystem. This is the ideal case, if all the cells are transmitting with maximumpower. However, it is not ideal, if all the base stations have the same maximumtransmit power level. Then, the small cells are creating more inter-cell interfer-ence for the adjacent cells, because of the high total transmit power. Therefore,the transmit powers of the cells have to be adapted in a way, that the interferencelevels do not exceed a certain threshold at the cell border. With this strategy(smaller cells for example have a lower total power limit than big cells) in thenetwork the worst case (=ideal case) will be reached.

In the strategy for CPICH coverage verification mode 2 the worst case is deter-mined by creating the highest possible interference level in the system with themaximum transmit powers of the cells and then adjusting the CPICH power lev-els of the cells in order to achieve the required coverage probability in the definedarea. The maximum transmit powers of the base stations are achieved by settingthe PCH value in the simulator to the maximum (40 dBm) and the SCH value ina way that the total transmit power of the cell (43 dBm) is reached.

Like in the CPICH coverage verification mode 1 an overcrowded scenario is usedagain for adjusting the CPICH power levels. However, when setting the totaltransmit powers of the cells to the maximum by increasing the SCH and thePCH, there will not be any mobile served. Therefore, for example 100 mobilesin each cell are investigated. For each mobile the CPICH power level of the


serving cell is adjusted in order to reach the received Ec/I0 requirement12. If therequirement is already fulfilled, the CPICH power remains unchanged.

Figure H.4 and Figure H.5 in Appendix H show the flowchart for the implemen-tation of the CPICH power adjustment for CPICH coverage verification mode2.

12For the simulations in Chapter 9 the Ec/I0 requirements are usually set to -12 dB (Sec-tion 7.2.2.2).


Chapter 9

Algorithm Performance Analysis

9.1 Introduction

In this second main chapter, the performance of the various algorithms, intro-duced in Chapter 8, are presented. Usually the big network scenario (see Sec-tion 7.4.1) is used for the analysis of the algorithms. If the small network scenario(7.4.2) is used, it is explicitly mentioned.

The results for the local algorithms Rule Based Approach, Simulated Annealingand Adaptive Rule Based Approach, are presented in Section 9.2, Section 9.3and Section 9.4, respectively. Section 9.5 presents the results for my GeneticAlgorithm. The analysis for the Analytic Optimization Algorithm is presentedin Section 9.6. Section 9.7 compares all the different algorithms.

Note that in general the results of the different sections cannot be compared witheach other, because during the development of the several algorithms also thenetwork simulator has been improved. However, results presented in one sectionare evaluated with the same version of CAPESSOTM .

9.2 Results for the Rule Based Approach

In this section the results for the Rule Based Approach (Section 8.2.1) are pre-sented. The network simulations are performed with CPICH coverage verificationmode 1 and the rule set from Appendix D is used. The results of the optimizationare shown in Table 9.1. Before optimization the number of served users is 483in the optimization area. After optimization with the Rule Based Approach, 817users are served. The GoS in both cases is about 95%. This increase in servedusers leads to a capacity gain of 69.5%.

125

126 CHAPTER 9. ALGORITHM PERFORMANCE ANALYSIS

Before optimization After optimization

Served users in 483 817

optimization area

Covered users in 512 856

optimization area

GoS [%] 94.3 94.5

Capacity gain [%] 69.5%

Computational effort 70

[iterations]

Table 9.1: Results for the Rule Based Approach with CPICH verification mode 1.

Further, the algorithm was executed with various number of iterations (see iterin Table D.1 in Appendix D). The default setting for the rule set is from 6 to 10.Table 9.2 shows the results. The table shows that there are not enough iterationsin the first case (4..8 iterations), while the best result (822) has been obtainedby using much more iterations.

Iterations Served users in optimization

area after optimization

4..8 765

5..9 807

6..10 (default) 817

7..11 807

8..12 810

9..13 822

10..14 819

Table 9.2: Results for the Rule Based Approach with CPICH verification mode 1.

9.2.1 Various Investigations with the Rule Based Approach

Based on the default rule set from Table D.1 in Appendix D, several investigationshave been carried out. In each investigation, one parameter was changed, whileall others were kept constant during the optimization process.

9.2. RESULTS FOR THE RULE BASED APPROACH 127

9.2.1.1 Optimization on Different Snapshots

In this investigation the Rule Based Approach was performed on several snap-shots with different user distributions. This means that before the optimizationa new user distribution was created and then the algorithm worked with this spe-cific distribution. During the optimization process the distribution of the usersremained the same.

Figure 9.1 shows the results for 20 different snapshots. In the legend of the figureStart serv and End serv denote the served users in the optimization area beforeand after the optimization. Start cov and End cov are the covered users. Insnapshot 1 the default user distribution is used (result from previous section).

Figure 9.1: Results for the Rule Based Approach on different snapshots.

The number of served and covered users are within a relatively close range. Theaverage number of served users values at algorithm start is 459. After optimiza-tion there are 791 served users in the optimization area on average (compared to483 and 817 for the result of the previous section).

9.2.1.2 Analyzing Optimization Results with Different Snapshots

A very important question is, how stable is an optimized network towards chang-ing user distributions. In this section results are presented with changing theuser distribution after the optimization process. For 5 different snapshots from


the section before (snapshot 1, 6, 11, 14 and 18; all shaded in grey in Figure 9.1)also after the optimization 20 snapshots are simulated with CAPESSOTM .

For using the optimized scenarios where the algorithm has performed well (snap-shot 1, 11 and 18) the results are shown in Figures 9.2, 9.3 and 9.4.

Figure 9.2: Results for the Rule Based Approach on different snapshots afteroptimization for snapshot 1.

As Figures 9.2, 9.3 and 9.4 show, the network is clearly optimized for the sce-nario generated by the receptive snapshot (if the network is originally optimizedfor snapshot 18, then the results are best if the same user distribution is usedafter optimization - see shaded areas in the figures). However, apart from thesemaxima, the results are relatively stable. This is important, since in a real sce-nario the users will move all the time, so it would make no sense to optimize thenetwork for a static distribution of users that is never allowed to change.

When changing the user distribution of badly optimized scenarios (snapshot 6and 14), the results are not so good. Figures 9.5 and 9.6 show the results.

In these cases, there is no such clear maximum in the results if the user dis-tribution is left unchanged - in some cases even better results are obtained bychanging it! This indicates that certain distributions of users exist that the al-gorithm cannot optimize efficiently. On the other hand, this can also be seen asan advantage: in those cases, the algorithm didn’t narrowly optimize for a veryspecific user distribution, and thus the results are worse in absolute numbers, butmore generally valid.








9.2.1.3 Optimization with Modified Parameter Ranges

In this evaluation, the allowed range for modifications of parameters (CPICHpower and antenna tilt) was adapted. Unless otherwise noted - and unlike in therule set from Appendix D - the indicated limits were valid throughout the wholesimulation (compared to different limits in each rule, see Table D.1). Table 9.3shows the individual results (bold limits indicate restrained parameter ranges,compare Table D.1).

CPICH power Antenna tilt Served users in Covered users in GoS [%]

[dBm] [◦] optimization area optimization area

15 8 816 855 93.9

20 8 765 805 94.7

20 7 756 794 95.1

25 8 730 767 95.2

15 7 820 863 93.4

15 5 718 754 95.2

25 5 694 730 95.1

24..20 5..8 776 816 95.0

20..10 5..9 774 814 92.1

10 6..10 716 754 94.9

Table 9.3: Results for the Rule Based Approach with different parameter ranges.

Applying constrains to the range of parameters mostly degrades the results. Ifthey need to be in place for some external reasons, the algorithm can still delivergood results; it is however preferable not to have such constrains.

Figure 9.7 shows a 3D-visualization of the optimization gain at combinations oflimited CPICH power and antenna tilt ranges.

Obviously, for the used network scenario, CPICH power modifications shouldbe allowed as far downwards as to 15 dBm, and antenna downtilts should beincreasable up to 8◦. If the range is too large, however, results deteriorate. Thisis mostly due to the algorithms’s linear design - in each step it only moves into onedirection and keeps the results if they are slightly better than the results obtainedin the previous step - even if a smaller step would have been more beneficial. Thegains obtained by a true bi-directional algorithm saturates at a higher level, evenif the allowed parameter ranges are too large (see results for the Adaptive RuleBased Approach in Section 9.4).


Figure 9.7: 3D matrix of parameter range limits for the Rule Based Approach.


9.2.1.4 Optimization on Adapted Start Scenarios

For a given scenario, it might be advantageous to modify certain attributes of allcells (e.g a downtilt of 3◦ instead of 0◦ for each antenna in the whole network).The purpose of these investigations was to run the algorithm on such an a prioriimproved scenario. Table 9.4 shows the initial values and end results for scenarioswith a fixed tilt on all antennas.

Initial values Results

Antenna tilt Served users in GoS [%] Served users in GoS [%]

[◦] optimization area optimization area

0 483 94.4 817 94.6

1 485 94.7 795 94.6

2 484 94.5 790 93.7

3 521 95.3 736 95.3

4 555 95.4 732 95.2

5 572 95.0 784 93.6

6 616 95.2 824 94.1

7 694 95.3 805 94.4

Table 9.4: Results for the Rule Based Approach with adapted antenna tilt in thestart scenario.

The column Served users in optimization area (Initial values) indicates the use-fulness of setting the downtilt to the indicated value for all cells (in this uniquescenario).

Similar simulations have been carried out, modifying the CPICH power. Theresults are presented in Table 9.5.

These results show that the developed Rule Based Approach depends on theinitial setting of the CPICH power and antenna tilt. The main reason for this isthat the algorithm is only able to go in one direction.

9.2.2 Runtime of the Rule Based Approach

For the evaluation a Pentium IV with a clock rate of 2GHz and 512MB mainmemory was used. The algorithm with the rule set from Appendix D and with afirst version of the network simulator (not runtime optimized) requires about 1 h30min for the 80 iterations.


Initial values Results

CPICH power Served users in GoS [%] Served users in GoS [%]

[◦] optimization area optimization area

33 483 94.4 817 94.6

32 485 94.7 815 94.6

31 486 94.9 807 93.8

30 494 95.2 833 94.0

29 510 95.0 814 94.1

27 521 95.3 776 94.1

25 524 95.1 767 94.5

Table 9.5: Results for the Rule Based Approach with adapted CPICH power inthe start scenario.

9.3 Results for Simulated Annealing

For the results with Simulated Annealing (Section 8.2.2) usually the rule set fromTable E.1 is used. The network simulations are performed with CPICH coverageverification mode 1 on the big network scenario (7.4.1). Before optimization thenumber of served users is 483 in the optimization area like in Section 9.2.

In this section results with both introduced cooling functions, Slow Cooling(Equ. 8.2) and Geometric Cooling (Equ. 8.1) are presented (Section 9.3.1 andSection 9.3.2). Further, results with the rule set from Table E.2 are presented(Section 9.3.3).

9.3.1 Results with Slow Cooling

For Slow Cooling an initial cooling temperature of 0.07 was used for TC . Thevalue of β was changed from 0.9 to 1.4. For each parameter three trials weredone. The results are shown in Table 9.6.

The results show that there is an important influence of the parameter β onthe cooling function. The best result (average of 3 runs), 858 served users inthe optimization area, has been obtained with β = 1.1. This corresponds to acapacity increase of 77.6%.

Table 9.7 show results with different initial values of TC . With this parameterthe shape of the cooling function can be changed, like by β. The shape of the

9.3. RESULTS FOR SIMULATED ANNEALING 135

Trial Cooling parameter Served users in Number of iterations Mean value

β optimization area served users

1 0.9 827 102

2 0.9 836 119 844

3 0.9 868 114

1 1.0 868 121

2 1.0 803 128 836

3 1.0 839 123

1 1.1 870 104

2 1.1 859 112 858

3 1.1 846 144

1 1.2 846 135

2 1.2 846 126 832

3 1.2 805 184

1 1.3 849 102

2 1.3 774 159 809

3 1.3 805 122

1 1.4 729 131

2 1.4 824 141 791

3 1.4 821 101

Table 9.6: Results for Simulated Annealing with Slow Cooling, different valuesfor β.


cooling function is a very important factor, because the probability of taking abad result heavily depends on TC . The parameter TC was varied from 0.03 to 1.For β a value of 1.1 was used.

Trial Initial temperature Served users in Number of iterations Mean value

for TC optimization area served users

1 0.03 718 163

2 0.03 801 131 790

3 0.03 852 140

1 0.04 710 155

2 0.04 767 157 760

3 0.04 804 190

1 0.05 851 122

2 0.05 860 117 843

3 0.05 828 127

4 0.05 834 123

1 0.07 870 104

2 0.07 859 112 858

3 0.07 846 144

1 0.1 829 132

2 0.1 821 126 808

3 0.1 773 157

1 0.5 854 104

2 0.5 829 133 837

3 0.5 828 131

1 1 866 103

2 1 860 87 852

3 1 829 111

Table 9.7: Results for Simulated Annealing with Slow Cooling, different valuesfor TC .

The best result I achieved with a initial value for TC of 0.07. So, I concludethat the best choice of the Slow Cooling parameters for the UMTS base station

9.3. RESULTS FOR SIMULATED ANNEALING 137

parameter optimization problem is, if β = 1.1 and the initial value of TC isset to 0.07. However, I have to mention that for one cooling parameter settingconsiderably more trials should be carried out. Due to the long runtime of thealgorithm it was only possible to make a few trials for one setting. With only 3runs a statistical significance cannot be seen.

9.3.2 Results with Geometric Cooling

For the simulations with the Geometric Cooling function (Equ. 8.1), the initialvalue of TC was set to 0.07. The parameter α was changed from 0.95 to 0.99. Foreach parameter three trials have been carried out. The rule type was the sameas in Section 9.3.1. The results are shown in Table 9.8.

From the table we see that the best results are obtained with a value for α of0.990. The algorithm found always the same solution, but always with a differentnumber of iterations.

If we compare Slow Cooling and Geometric Cooling, we can conclude that Geo-metric Cooling provides slightly better results.

9.3.3 Results with Improved Rule Set

I extended the rule set from Table E.1 by two additional rules (Table E.2). Now,the algorithm is able to adjust the CPICH power and antenna tilt parameter witha higher resolution.

Both cooling functions with the following settings have been tested:

• Slow Cooling: Initial value for TC : 0.07, β = 1.1

• Geometric Cooling: Initial value for TC : 0.07, α = 0.990

With each cooling function three simulations have been carried out, to see if theresults are independent of the Simulated Annealing random value (probabilityfor the choice of bad results). Table 9.9 shows the results of the simulations.

From the table we see again that Geometric Cooling outperforms Slow Cooling.The best result was found with Slow Cooling, however Geometric Cooling foundin each trial the same good result.


Trial Cooling parameter Served users in Number of iterations Mean value

α optimization area served users

1 0.950 717 145

2 0.950 740 140 729

3 0.950 731 106

1 0.960 727 107

2 0.960 736 149 738

3 0.960 750 128

1 0.970 846 136

2 0.970 821 135 839

3 0.970 849 96

1 0.975 832 121

2 0.975 846 126 847

3 0.975 863 100

1 0.980 855 116

2 0.980 866 102 862

3 0.980 866 108

1 0.985 860 89

2 0.985 830 113 852

3 0.985 867 95

1 0.990 866 105

2 0.990 866 96 866

3 0.990 866 123

Table 9.8: Results for Simulated Annealing with Geometric Cooling.

9.4. RESULTS FOR THE ADAPTIVE RULE BASED APPROACH 139

Cooling function Trial Served users in Number of iterations Mean value

optimization area served users

1 886 188

Slow Cooling 2 831 131 850

3 834 153

1 879 143

Geometric Cooling 2 879 162 879

3 879 159

Table 9.9: Results for Simulated Annealing with improved rule set.

9.3.4 Runtime of Simulated Annealing

For the evaluation a Pentium IV with a clock rate of 2GHz and 512MB mainmemory was used. The two rule sets have a little bit different runtime. Ruleset 1 (see Table E.1) requires on average 124 iterations, and therefore the averageruntime is about 2 h 10min. Rule set 2 (see Table E.2) needs on average 156iterations, and therefore the average runtime is about 2 h 50min.

9.4 Results for the Adaptive Rule Based Ap-

proach

As described in Chapter 8 (Section 8.2.3), the implemented Adaptive Rule BasedAlgorithm is limited by a set of rules, which restrict the changes of CPICH powerand antenna tilt per adjustment. In this section the results are presented for thefour developed rules sets (Appendix F). The differences in the four rule sets arethe step size and parameter (CPICH power and antenna tilt) limitation settings.The results in this section are obtained with CPICH coverage verification mode 1.

Before and after optimization 50 snapshots with different user distributions aresimulated. The optimization process itself is performed with one fixed user distri-bution. Before optimization, the mean number of served users in the optimizationarea is 511. Figure 9.8 shows the block diagram for the simulation.

Figure 9.9 gives the cumulative distribution function (cdf) of the achievable num-ber of served users in the optimization area, at a GoS of 95% over the 50 snap-shots. It shows that different rule sets have significant influence on the optimiza-tion result.


Figure 9.8: Block diagram for the simulation of the four rule sets over 50 snap-shots.

Figure 9.9: Comparison of cdf curves for the four rules sets of the Adaptive RuleBased Approach (50 snapshots).

9.5. RESULTS WITH THE GENETIC ALGORITHM 141

The fluctuation of the cdf curves suggests that different rule sets favor differentrealizations of the user distribution. There is no particular rule set that is ableto achieve the highest number of served users in all 50 snapshots. Nevertheless,the gradient of the cdf curves illustrates that ”rule set #4”gives the most stableperformance among the four different rule sets.

Additionally, the mean achieved number of served users for each rule set, shownin Table 9.10, indicates that ”rule set #4”has the highest mean number of servedusers and therefore yields to a capacity gain of 66.9%.

Simulation Mean # of served Standard deviation Capacity

users in target area (# served users) gain [%]

Before optimization 511 7.54 (1.48%)

Rule set #1 831 31 (3.7%) 62.6%

Rule set #2 839 29 (3.5%) 64.2%

Rule set #3 842 34 (3.9%) 64.8%

Rule set #4 853 25 (2.9%) 66.9%

Table 9.10: Results for Adaptive Rule Based Approach with CPICH verificationmode 1.

It is noticeable that the standard deviation of served users with respect to differentuser distributions after the optimization is much higher than before. This meansthat the optimized network is less stable regarding the user distribution.

In this thesis only a small part of the simulations are shown. In [23] more resultsand a stability analysis of the algorithm are presented.

The runtime of the Adaptive Rule Based Algorithm depends on the used rule set.On average the algorithm requires about 150 iterations. On a Pentium IV witha clock rate of 2GHz and 512MB main memory the runtime is approximate 2 h45min.

9.5 Results with the Genetic Algorithm

The Genetic Algorithm has been tested on both scenarios (small and big net-work scenario) as well as with both CPICH coverage verification modes. Severaldifferent settings of the parameters for the Genetic Algorithm, like size of the pop-ulation, selection pressure and parameters for the local optimization have beentested. Table 8.4 in Section 8.3.3 shows the best settings. However, good results


have also been obtained with other settings. As fitness function, the extendedfitness function from Equ. (6.2) was used.

9.5.1 CPICH Coverage Verification Mode 1

9.5.1.1 Results on the Small Network Scenario

For the small network scenario altogether 60 optimization runs have been carriedout, always with the same user distribution. Before the optimization 347 servedusers are in the network. Figure 9.10 shows the results.

In Figure 9.10 the results are in chronological order of execution. From the figurewe see that during the development process the results got better. The meannumber of served users over all 60 runs are 488. This corresponds to a capacityincrease of 40.6%. The standard deviation is 17.6 (3.6%).

The best result was achieved with the optimization run at index 53. After thisoptimization run 518 users were served, this corresponds to a capacity increaseof 49.3%. Figure 9.11 shows the growth of the fitness value (fitness value of thebest individual and average fitness of the population) during the optimization forthis run and Table 9.11 presents the used parameters for the Genetic Algorithm.



Local optimization:





Antenna downtilt 0◦ ÷ 8◦

Table 9.11: GA settings for the best optimization run on the small networkscenario with CPICH verification mode 1.

For the best optimization run the algorithm terminates after 172 populations.However, Figure 9.11 shows that the final result is already found after 75 popula-tions. Afterwards, no better results were found. To evaluate the stability of theresult, the CPICH power and antenna tilt settings before and after optimizationwere evaluated with 100 different user distribution snapshots. Table 9.12 showsthe results of the analysis.


Figure 9.10: Results for the Genetic Algorithm on the small network scenariowith CPICH verification mode 1.


Figure 9.11: Optimization run for the best result on the small network scenariowith CPICH verification mode 1.

Number of served users Before optimization After optimization

in optimization area

Min 323 460

Max 363 518

Mean 344 483

Standard deviation 7.2 (2.1%) 9.1 (1.9%)

Capacity Gain [%] 40.4

Table 9.12: Evaluation of the best result with 100 different snapshots on thesmall network scenario with CPICH verification mode 1.


The capacity increase for the mean number of served users is only 40.4%. How-ever, from Table 9.12 we see that the result after optimization is also stable likebefore optimization, because the standard deviation does not increase. Further-more we see that the optimization has been carried out for one single user dis-tribution and so during the evaluation with different user distributions no higherresult than 518 served users was found.

9.5.1.2 Results on the Big Network Scenario

For the big network scenario altogether 17 optimization runs have been carriedout, again always with the same user distribution. Before the optimization 506served users are in the optimization area. Figure 9.12 shows the results, again inchronological order of execution.

From Figure 9.12 we see again that optimization runs, which were carried outlater in the development process, deliver better results. The mean number ofserved users over all 17 runs are 1064. This corresponds to a capacity increase of110.3%. The standard deviation is 77.8 (7.3%).

The best result was achieved with the optimization run with index 17. Afterthis optimization run, 1148 users were served in the optimization area; this cor-responds to a capacity increase of 126.9%. Figure 9.13 shows the growth of thefitness value during the optimization for this run and Table 9.13 presents theused GA parameters.



Local optimization:






Table 9.13: GA settings for the best optimization run on the big network scenariowith CPICH verification mode 1.

The algorithm runs altogether 335 populations. However, like for the small net-work scenario the best result was already found after 198 populations. FromFigure 9.13 we again see (like in Figure 9.11) the biggest increase of the fitness


Figure 9.12: Results for the Genetic Algorithm on the big network scenario withCPICH verification mode 1.


Figure 9.13: Optimization run for the best result on the big network scenariowith CPICH verification mode 1.

in the first part of the optimization process (up to population 100). Afterwards,only a small increase of the fitness values per population is observed. To evalu-ate the stability of the result, again the CPICH power and antenna tilt settingsbefore and after optimization were evaluated with 100 different user distributionsnapshots. Table 9.14 shows the results.



Min 485 1044

Max 529 1148

Mean 511 1128



Table 9.14: Evaluation of the best result with 100 different snapshots on the bignetwork scenario with CPICH verification mode 1.

The capacity increase for the mean number of served users is 120.7% and the


result after optimization is stable again, like before optimization.


All results, which have been performed with the CPICH coverage verificationmode 2 were analyzed before and after the optimization with 100 different userdistribution snapshots. However, the optimization itself has been carried outwith one fixed user distribution. For the CPICHEc/I0 threshold a value of -12 dBis used.

9.5.2.1 Results on the Small Network Scenario

Figure 9.14 shows the results on the small network scenario. Altogether, 7 differ-ent optimization runs have been carried out. Before the optimization in average352 users are served in the network. The mean number of served users after theoptimization over all 7 runs are 374. This corresponds to a capacity increase of6.3%. The standard deviation is 7.6 (2.0%).

Index 5 in Figure 9.14 shows the best result. After this optimization run 382users are served in average, this corresponds to a capacity increase of 8.5%. Ifwe compare the results from CPICH coverage verification mode 2 with mode 1,we see that now the capacity increase is much lower. This is due to the factthat for mode 2 the interference is included in the calculation and so the networksimulator delivers more pessimistic results.

Table 9.15 summarizes the optimization results and Table 9.16 presents the usedparameters. Again, like for the CPICH coverage verification mode 1 the growthof the fitness value during the optimization run is shown for the best run (index5 in Figure 9.14). See Figure 9.15 for the diagram.

Figure 9.15 shows that the optimization run terminates after 452 populations.Only one user distribution has been used during the optimization process. Thestarting value is 351 served users and the final value is 397 served users. Thebest fitness value is reached after 416 populations. If we compare the curvefrom Figure 9.15 with the fitness growth of CPICH coverage verification mode 1(Figure 9.11), we see that now the increase in fitness is slower. Remember, inFigure 9.11 the best result was found after 75 populations.

9.5.2.2 Results on the Big Network Scenario

For the big network scenario altogether 21 optimization runs have been carriedout. Before the optimization in average 512 users are served in the optimizationarea. Figure 9.16 shows the results.


Figure 9.14: Mean results over 100 snapshots for the Genetic Algorithm on thesmall network scenario with CPICH verification mode 2.




Min 334 366

Max 369 397

Mean 352 382



Table 9.15: Evaluation of the best result with 100 different snapshots on thesmall network scenario with CPICH verification mode 2.



Local optimization:






Table 9.16: GA settings for the best optimization run on the small networkscenario with CPICH verification mode 2.


Figure 9.15: Optimization run for the best result on the small network scenariowith CPICH verification mode 2.

In Figure 9.16 the results are in chronological order of execution. From the figurewe see that during the development process the results get better. The meannumber of served users over all 21 runs are 656. This corresponds to a capacityincrease of 28.1%. The standard deviation is 22.4 (3.4%).

The best result was achieved with the optimization run with the index 16. Afteroptimization 693 users are served in average. This corresponds to a capacityincrease of 35.4%. Again, like for the small network scenario we see that thenetwork simulator is more pessimistic and so the capacity gain is much lowerthan with the CPICH coverage verification mode 1. Remember, with CPICHverification mode 1 we had a capacity increase of 120%! However, we have to keepin mind that the results of the CPICH coverage verification mode 2 approximatesthe real situation better.

The optimization results for the best run are summarized in Table 9.17 and theGA parameters in Table 9.18. Figure 9.17 shows the the growth of the fitnessvalue during the optimization run.

In Figure 9.17 the starting value of the optimization are 498 served users and thetermination value are 733 served users in the optimization area. The optimizationruns 338 populations. From the figure we see that first there is a very sharp rise infitness. Then, from population 150 to 280 no better result is found. Afterwards,


Figure 9.16: Mean results over 100 snapshots for the Genetic Algorithm on thebig network scenario with CPICH verification mode 2.




Min 498 672

Max 530 733

Mean 512 693



Table 9.17: Evaluation of best result with 100 different snapshots on the bignetwork scenario with CPICH verification mode 2.



Local optimization:





Antenna downtilt −2◦ ÷ 10◦

Table 9.18: GA settings for the best optimization run on the big network scenariowith CPICH verification mode 2.


Figure 9.17: Optimization run for the best result on the big network scenariowith CPICH verification mode 2.

the algorithm finds a new solution and so gets a new impulse. The best resultis then found after 320 populations. From the figure we see also that, if we areonly interested in a good result and do not want to wait until the algorithm isconverged, the algorithm can be stopped after 150 populations. So, we save a lotof computation time.

9.5.3 Runtime of the Genetic Algorithm

The runtime of the Genetic Algorithm depends on the network scenario and onthe size of the population. For the presented results two different populationsizes were used: 100 and 400 individuals. On a Pentium IV with a clock rate of2GHz and 512MB main memory the Genetic Algorithm runs on the big networkscenario with 100 individuals about 15 h and with 400 individuals about 65 h.The small network scenario requires approximately half of the time of the bignetwork scenario. The version of the network simulator, which was used for theGenetic Algorithm was optimized in runtime and therefore much faster than thefirst version, used for the local algorithms.

9.6. RESULTS FOR THE ANALYTIC OPTIMIZATION ALGORITHM 155

9.6 Results for the Analytic Optimization Algo-

rithm

For obtaining the results with the Analytic Optimization Algorithm, both CPICHcoverage verification mode 1 and mode 2 have been used. The two main results inthe following Sections 9.6.1 and 9.6.2 are the number of served users in the opti-mization area (nr served target) and the mean number of served users in the op-timization area over 40 different user distribution snapshots (mean cap target).

The reference scenario is characterized by antenna downtilts of 0◦ (+3◦ electri-cal tilt in the antenna pattern) for all the antennas, and CPICH power levelsof 33 dBm in all the cells in the scenario. In the Tables 9.19, 9.20 and 9.21,four columns of results are presented. The first column shows the results of thereference scenario. The other three columns titled Adj azimuth, Adj tilt andAdj CPICH contain then results after running the strategy for the adjustment ofthe antenna azimuth, after the additional strategy for the antenna downtilt andafter the additional CPICH adjustment strategy, respectively.

In the adjustment routine for the antenna azimuth, three critical spots1 inside theoptimization area were used. In the strategy for the adjustment of the antennadowntilt, the initial tilt value was set to 3◦ (+3◦ electrical tilt) in all cells, while theinitial CPICH power value (start CPICH) was varied for the diverse simulations.An initial CPICH power level (start CPICH) is also necessary for the CPICHadjustment strategy. The actually used initial CPICH power level for the specialsimulation is given in the additional notes in Tables 9.19, 9.20 and 9.21.

In the simulations, the required coverage probability for the worst case in the totalscenario was set to 50% and in the optimization area to 75%, or 80% in the totalscenario and 98% in the optimization area, as again indicated in the additionalnotes in the various tables. In CPICH coverage verification mode 2 the coverageprobability is indicated as CPICHEc/I0 cov prob total or CPICHEc/I0 cov prob target,while in CPICH coverage verification mode 1 simply as cov prob total or cov prob target.For the overcrowded scenario in antenna tilt and CPICH power strategies, 5000mobiles were used.

In Figures 9.18 to 9.19, the mean numbers of served users in the optimization areafrom the Tables 9.19, 9.20 and 9.21 are presented in bar charts. The bar chart forCPICH coverage verification mode 1 (Figure 9.18) shows the mean capacity in theoptimization area in the reference scenario (initial parameter setting) in blue, aswell as after the adjustment of base station azimuth (green bar), antenna downtilt(yellow bar) and CPICH power level (red bar). The bar chart for CPICH coverageverification mode 2 (Figure 9.19) presents an additional bar for the variation of

1For the definition of a critical spot see Section 8.4.1.


the required coverage probability for the worst case in the optimization area.Thus, in CPICH coverage verification mode 2 there are two different bars for themean number of served users in optimization area after the CPICH adjustmentroutine, one for required coverage probability in the total scenario of 0.8 and0.98 in the optimization area, and another for the required coverage probabilityin the total scenario of 0.5 and 0.75 in the optimization area. It is importantto remember that these required coverage probabilities are only for the worstcase. The effective coverage probabilities in normal interference situation arepresented in the Tables 9.19, 9.20 and 9.21 and are between 0.91 and 1, both intotal scenario and in the optimization area.


As Figure 9.18 shows, the mean capacity in the optimization area can be increasedfrom 512 mean served users in the reference scenario (blue bar) up to 829 meanserved users (red bar). This equals a gain of 62% in mean capacity comparedto the reference scenario. It is important to mention that after the CPICHadjustment the CPICH power level in all cells is 15 dBm, equal to the minimumCPICH power level. It is also shown in Table 9.19 that the reached coverageprobability after the CPICH strategy in the total scenario (cov prob total) is morethan 91% and in the optimization area (cov prob target) is 100%, even thoughthe required coverage probability was set to 0.8 and 0.98 in the total scenario andin the optimization scenario, respectively. However, if using a parameter settingwith a CPICH power level of 33 dBm in all cells, thus utilizing the routineswithout the CPICH procedure, there is also an increase in mean capacity in theoptimization area from 512 to 703 mean served users (yellow bar in Figure 9.18).This is an improvement of 37% compared to the reference scenario.

Reference scenario Adj azimuth Adj tilt Adj CPICH

nr served target 508 540 691 821

mean cap target 512 557 703 829

cov prob total 1 0.9963 0.9904 0.9193

cov prob target 1 1 1 1

additional notes start CPICH in adj tilt: 15 dBm, start CPICH in adj CPICH: 15 dBm,final CPICH values = 15 dBm in all cells, required coverage probability: 0.8total and 0.98 in target area, best server equal dist.

Table 9.19: Results for the Analytic Optimization Algorithm with CPICH veri-fication mode 1 (40 snapshots).

9.6. RESULTS FOR THE ANALYTIC OPTIMIZATION ALGORITHM 157

Figure 9.18: Results for the Analytic Optimization Algorithm with CPICH veri-fication mode 1 (40 snapshots).


For this mode the results are shown in Table 9.20 for a required coverage prob-ability in worst case of 0.5 in the total scenario and 0.75 in the optimizationarea. Before parameter adjustment the mean number of served users is 520 inthe reference scenario. The improvement of capacity in the optimization area,which can be reached by using CPICH coverage verification mode 2 is 15% (to595 served users) compared to the reference scenario after adjusting the antennaazimuth and of 21% (to 630 served users) after the antenna tilt routine. Afterthe procedure for adjustment of CPICH power levels, however, Table 9.20 showsa decrease in capacity. This is due to the fact that for reaching the coverage re-quirements in the worst case with quite severe CPICHEc/I0 threshold of -12 dB,the CPICH power levels have to be increased from the initial value of 33 dBm inthe reference scenario. The final CPICH power levels after CPICH adjustmentare between 32 dBm and 35 dBm. If using the more severe requirements in cover-age probability of 0.8 in the total scenario and 0.98 in the optimization area, thefinal results goes down even more to 553 served users in the optimization area(see Table 9.21). The final CPICH power levels in this case are between 35 dBmand 37 dBm. Figure 9.19 shows the results in a diagram.

Comparing the results from CPICH coverage verification mode 1 and mode 2,we can see that the possible improvement by optimization is smaller in the morerealistic case (mode 2), because the CPICH coverage verification is including


Reference Adj azimuth Adj tilt Adj CPICH

scenario



CPICHEc/I0 cov prob total 0.9339 0.9288 0.9433 0.9598

CPICHEc/I0 cov prob target 0.9545 0.9533 0.9635 0.9860

additional notes start CPICH in adj tilt: 36 dBm, start CPICH in adj CPICH:32 dBm, final CPICH values: ' 32-35 dBm, required coverage proba-bility in worst case: 0.5 total and 0.75 in target area, best server equaldist., CPICHEc/I0 threshold = −12 dB

Table 9.20: Results for the Analytic Optimization Algorithm with CPICH veri-fication mode 2 (40 snapshots) with CPICHEc/I0 threshold -12 dB and requiredcoverage probability in worst case of 0.5/0.75.

Reference Adj azimuth Adj tilt Adj CPICH

scenario



CPICHEc/I0 cov prob total 0.9339 0.9288 0.9433 0.9823

CPICHEc/I0 cov prob target 0.9545 0.9533 0.9635 1

additional notes start CPICH in adj tilt: 36 dBm, start CPICH in adj CPICH:32 dBm, final CPICH values: ' 35-37 dBm, required coverage proba-bility in worst case: 0.8 total and 0.98 in target area, best server equaldist., CPICHEc/I0 threshold = −12 dB

Table 9.21: Results for the Analytic Optimization Algorithm with CPICH veri-fication mode 2 (40 snapshots) with CPICHEc/I0 threshold -12 dB and requiredcoverage probability in worst case of 0.8/0.98.

9.7. COMPARISON OF THE VARIOUS APPROACHES 159

Figure 9.19: Results for the Analytic Optimization Algorithm with CPICH veri-fication mode 2 (40 snapshots) with CPICHEc/I0 threshold -12 dB.

interference calculation.

9.6.3 Runtime of the Analytic Optimization Algorithm

The Analytic Optimization Algorithm requires only 5 network evaluations. There-fore the runtime is very fast. On a Pentium IV with a clock rate of 2GHz and512MB main memory the algorithms requires with the fast version of the networksimulator only about 1min.

9.7 Comparison of the Various Approaches

In this section a comparison of the individual algorithms is given. The used bignetwork scenario is evaluated with CPICH coverage verification mode 1. Ta-ble 9.22 shows the results for 50 user distribution snapshots and in Figure 9.20the corresponding diagram is presented.

From Table 9.22 we see that the Genetic Algorithm performs best. It outperformsall other algorithms easily. Beside the best optimization results we also canconclude that the GA delivers stable results. This means that the resulting


Number of served Number of

Simulation users in Capacity gain network

optimization area [%] evaluations

Before optimization 511

Algorithm

Rule Based Approach 806 57.7 80

Simulated Annealinga 815 59.5 ∼ 150

Adaptive Rule Based 853 66.9 ∼ 150

Approach

Genetic Algorithmb 1046 104.7 ∼ 150000

Analytic Optimization 821 60.7 5

Algorithm

Table 9.22: Comparison of the different algorithms with 50 different snapshotson the big network scenario with CPICH verification mode 1.a The presented value is the result of only one run. However, for a fair comparisonthe mean value of several runs should be calculated.b The presented value is the mean value over 17 different runs.

9.7. COMPARISON OF THE VARIOUS APPROACHES 161

Figure 9.20: Comparison of the different algorithms on the big network scenariowith CPICH verification mode 1.


parameter settings also works fine with different user distribution snapshots. Forthe other algorithms I cannot claim this. However, the Genetic Algorithm hasone significant drawback, its runtime. The computational effort is much higherthan for all other algorithms.

If we compare the local optimization techniques, Rule Based Approach, SimulatedAnnealing2 and Adaptive Rule Based Approach, we see that the Adaptive RuleBased Approach shows the best result. The computation effort is for all threealgorithms approximately the same.

Comparing the result of the Analytic Optimization Algorithm with the localtechniques, we see that almost the same improvement is reached. It is importantto mention that the computational effort with only 5 network evaluations is muchsmaller than for all other optimization algorithms.

Finally, in this chapter I conclude that if the computational effort is irrelevant,then the Genetic Algorithm is the best choice for optimizing the base stationparameters in a UMTS network. However, if the runtime of the algorithm isvery important, the Analytic Optimization Algorithm shows a good performance,because in only 5 evaluations a good result is achieved. If the computational effortplays only an underpart, then the Adaptive Rule Based Approach is a good choice,because with about 150 network evaluations the second best result is obtained.

2For a fair comparison a mean value of several runs should be calculated.

Chapter 10

Summary and Conclusion

In this thesis I addressed the problem of capacity optimization in UMTS FDDnetworks. The goal was to improve the capacity of the network, measured asserved users, without any additional expenses: The capacity should be improvedonly by changing the parameters of the base stations.

For the operation of a network, a specific number of base station parametersinfluence the capacity and therefore affect the performance of the network. Eachsector of a base station can be configured by selecting: antenna type, antenna tilt,antenna pattern or CPICH power. All these parameters have a strong influenceon the interference in the system and therefore on the amount of served users.

In this thesis I focused on the optimization of CPICH power and antenna tilt,because these parameters have the most influence [54, 88]. By optimizing theantenna tilt settings, the other-to-own-cell interference ratio can be reduced: Theantenna main beam delivers less power towards the neighboring base stations,and therefore most of the radiated power goes to the area that is intended to beserved by this particular base station. Also the CPICH power settings are veryimportant: The CPICH power has to be set such that the coverage is ensured withminimum interference to neighboring cells in order to reduce the pilot pollution.

Altogether five different algorithms has been developed. The first three opti-mization algorithms, Rule Based Approach, Simulated Annealing and AdaptiveRule Based Approach are local techniques. Also a global technique, the GeneticAlgorithm, has been developed. The last algorithm is an analytic approach.

The Rule Based Approach starts from a scenario where all cells of the networkare set to identical values for CPICH power and antenna tilt. The optimizationprocess is characterized by reducing the CPICH power and increasing the antennadowntilt in the individual cells according to a configurable rule set.

Based on the Rule Based Approach the algorithm was subsequently extended and

163

164 CHAPTER 10. SUMMARY AND CONCLUSION

improved by incorporating Simulated Annealing. In contrast to the first method,the decision to take a bad result is independent of the rule set.

The third algorithm was developed within the scope of a master thesis and is alsoa further development of the Rule Based Approach. The main difference betweenthe Adaptive Rule Based Approach and the other two local approaches is thatCPICH power and antenna tilt are changed together, and that also an increase ofCPICH power and antenna up-tilting is possible during the optimization process.

In the next step a Genetic Algorithm was investigated and adapted for the UMTScapacity optimization problem. I implemented a representation scheme, whichcan also be utilized for optimizing other base station parameters. The geneticoperators were adapted for the UMTS capacity optimization problem by consid-ering the quality of the network. In addition, a simple local optimization wasimplemented, which is applied to the best individuals to improve their fitness.

The last discussed optimization algorithm is the Analytical Optimization Algo-rithm. This adjustment routine was developed within the scope of a masterthesis. Besides antenna tilt and CPICH power settings, the ’ad hoc’ algorithmalso optimizes antenna azimuth. ’Ad hoc’ in this context means to reach theresult only by considering the structure of the UMTS network. Thus, in contrastto the other step by step optimization strategies only a few optimization stepsare necessary.

A fitness function is needed to represent the optimization goal. In this thesis, Iconsidered the number of served users as the goal of the optimization. For theGenetic Algorithm I used a fitness function, which also includes coverage andsoft handover in addition to the capacity, in order to represent the quality of thesolutions more accurately.

For the evaluation of the network configuration and in consequence for the eval-uation of the fitness function, a network simulator is necessary. I used the staticUMTS FDD network simulator CAPESSOTM from SYMENA, Software & Con-sulting GmbH, for these evaluations. The simulator allows two different modes:CPICH coverage verification mode 1 with a fixed threshold for CPICH coverage,and CPICH coverage verification mode 2 including the interference by an EC/I0

threshold.

The analysis of the individual algorithms was done on two virtual scenarios of atypical European city. In the first scenario the network covers the whole area ofthe city. The second scenario contains only downtown.

With the different algorithms I achieved improvements in capacity of up to 105%for CPICH coverage verification mode 1 and up to 35% CPICH coverage verifi-cation mode 2. In both modes the Genetic Algorithm performs best, but withthe drawback of a high computation time. The Rule Based Approach showed the

165

worst performance. The capacity increase was about 60% for CPICH coverageverification mode 1.

So, I conclude for the developed algorithms that if the computationaleffort is irrelevant, the Genetic Algorithm is the best choice for op-timizing the base station parameters in a UMTS network. However,if the runtime of the algorithm is very important, the Analytic Opti-mization Algorithm should be used, because this algorithm shows goodresults in only 5 network evaluations.

166 CHAPTER 10. SUMMARY AND CONCLUSION

Chapter 11

Appendix

167

168 CHAPTER 11. APPENDIX

Appendix A

UMTS - Network Structure

In this appendix an overview over the basic entities of the UMTS network is given.The overview is based on the standard presented in [1]. The basic configurationof a Public Land Mobile Network (PLMN) is shown in Figure A.1.

In the basic configuration presented in Figure A.1, all the functions are consid-ered implemented in different equipments. Therefore, all the interfaces withinPLMN are external. Interface A and Abis are defined in the GSM 08-series ofthe Technical Specifications (TS). Interfaces Iu, Iur are defined in the UMTS25.4xx-series of Technical Specifications. Interfaces B, C, D, E, F and G needthe support of the Mobile Application Part of the signalling system No. 7 toexchange the data necessary to provide the mobile service. No protocols for theH-interface and for the I-interface are standardized. All the GPRS-specific in-terfaces (G-series) are defined in the UMTS 23-series and 24-series of TechnicalSpecifications. Interfaces Mc, Nb and Nc are defined in UMTS 23.205 and in theUMTS 29-series of Technical Specification1.

From this configuration, all the possible PLMN organizations can be deduced.In the case when some functions are contained in the same equipment, the rel-evant interfaces become internal to that equipment. The individual blocks andfunctionalities of the Core Network (CN) are well presented in [1]. The CN canuse two different types of access networks: the base station system (BSS) andthe radio network system (RNS). The MSC (respectively SGSN) can connect toone of these Access Network (AN) type or to both of them2.

For the network optimization the functionalities in, and the interfaces between

1All specifications can be found on the 3gpp server, http://www.3gpp.org.2The access technologies offered by the BSS are described in the 45-series of 3GPP specifi-

cations. The access technologies offered by the RNS (FDD, TDD) are described in the 25-seriesof 3GPP specifications (www.3gpp.org).

169

170 APPENDIX A. UMTS - NETWORK STRUCTURE

RNS and MS are of interest3. In the network simulator from SYMENA, Software& Consulting GmbH, which is used throughout this thesis, the functionalitiesof the radio network controller (i.e. SHO, RRM,...) are modeled under theassumption that there is no difference between two different RNCs. This meansthat the Iur-Interface is not considered.

3The interface between the MS and the RNS is specified in the 24- and 25-series of UMTSTechnical Specifications (www.3gpp.org).

171

Figure A.1: Overview and basic entities of the UMTS network structure.

172 APPENDIX A. UMTS - NETWORK STRUCTURE

Appendix B

3GPP COST 259 ChannelModels

This appendix is based on the deployment aspects for channel models in [7].Within 3GPP only a small portion of the COST 259 models [27] are included.Nevertheless, in the following description the 3GPP point of view is presented.

COST 259 [27] is a research forum founded by the EU, in which there are par-ticipants from manufactors, operators and universities. This forum is a successorof COST 207 [36] and COST 231 [28], which did the work on which the channelmodels used in GSM standardization were based. One of the work items iden-tified in COST 259 was to propose a new set of channel models that overcomethe limitations in the GSM channel models, while aiming at the same generalacceptance. The models are aimed at UMTS and HIPERLAN, with particularemphasis on adaptive antennas and directional channels.

B.1 Model Descriptions

The main difference between the COST 259 model and previous models is that ittries to describe the complex range of conditions found in the real world by dis-tributions of channels rather than a few ”typical”cases. The probability densitiesfor the occurrence of different channels are functions of mainly two parameters:

1. Environment

2. Distance

Given a certain environment (e.g. Urban Macrocell) and a certain distance (ordistance range/cell radius), the parameters describing the distribution functions

173

174 APPENDIX B. 3GPP COST 259 CHANNEL MODELS

for this particular case can be extracted. Performing a sufficient number of chan-nel realizations will give a distribution of channels, which give a much betterrepresentation of reality than what would be possible using only one channel.

The environments identified in COST 259 and included in 3GPP so far are givenin Table B.1. The macrocellular environments have the same names as the GSMmodels. Further parameters and a much more detailed description of the modelcan be found in [27, 98].

Macrocell Microcell Picocell

Typical Urban (Street Canyons) (Tunnel/Corridor)

Bad Urban (Open Places) (Factory)

Rural Area (Tunnels) (Office/Residential Home)

Hilly Terrain (Street Crossings) (Open Lounge)

Table B.1: Preliminary environments identified by COST 259.

In COST 259, a number of properties of the propagation channel has been con-sidered in the model work. The full proposal includes all of these properties,but it is quite simple and straightforward to implement the model in a modularstructure, so that each of the properties can be switched on or off individuallydepending on the application. Inherent in the model are also correlations be-tween the properties, e.g. time dispersion and shadow fading are modeled asbeing partially correlated.

B.2 3GPP Considerations

The propagation properties considered in the COST 259 model and consideredby 3GPP are shown in Table B.2.

The shape of the channel is given by one or several clusters, where each clusteris exponentially decreasing in delay and Laplacian (double-sided exponential) inazimuth. Each cluster consists of a number of Rayleigh-fading paths, plus apossible non-fading path to get Rice fading.

Of interest here are mainly the properties 4 and 7 shown in Table B.2. For thiscase, a full description of the channel is given by specifying the parameter set (seeFigure B.1). The ith cluster is described by its total power Pi, the delay of thefirst path τi and the cluster delay spread στ,i. The last parameter describes theslope of the exponentially decaying power in the cluster. The number of clusterspresent is given by NC .

B.2. 3GPP CONSIDERATIONS 175

1 Path Loss

2 Shadow Fading

3 Fast Fading

4 Time Dispersion

5 Angular dispersion (azimuth and/or elevation at BS)

6 Polarization

7 Multiple Clusters

8 Dynamic channel variations (variations 1-7)

Table B.2: Propagation properties proposed by COST 259 and considered by3GPP.

Figure B.1: Channel shape (power delay profile) with multiple clusters. Source:[7].

176 APPENDIX B. 3GPP COST 259 CHANNEL MODELS

From the 3GPP point of view it is possible to reduce the complexity of the COST259 model by approximating the continuous distributions with a small number ofcases, selected to be typical representations of the channel in common environ-ment. 3GPP proposes a set of models with fixed parameters as shown in FigureB.2. The selected parameters correspond to the COST 207/GSM models withone important difference concerning the delay spread value for the Typical Urbanchannel. This has been reduced to better correspond to typical measurementresults.

Figure B.2: Reduced complexity channel model parameters. Source: [7].

A cluster in the models outlined here is represented by a number NP independentRayleigh-fading paths with classical Doppler spectrum, randomly distributed inthe interval [τi, τi + k · στ,i]. Preliminary assignments are NP = 20 and k = 4.

For the evolution of the optimization algorithms in this thesis, an improved ver-sion of this COST 259 model is used. A data set with all the pathloss informationwas kindly provided by Dipl. Ing. Dr. Hermann Buhler GmbH, Modling, Autria.

Appendix C

RAKE Reception

In this appendix the details about the RAKE implementation for UMTS areexplained. The basic operations for CDMA signal reception can be described inthree steps:

• Time delay identification:First of all the different time delays at which significant energy arriveshave to be identified. With this, the correlation receivers, i.e. the RAKEfingers, have to be allocated to the individual peaks. Quoted from [54], themeasurement grid for acquiring the multipath delays is in the order of onechip duration (typically within the range of 1

4- 1

2chip duration) with an

update rate in the order of some tens of milliseconds.

Note that the chip duration in UMTS is Tc = 0.26 µs. With this, multi-path components with a difference in the path length of at least 0.26 µscan be separated and combined coherently. This difference in path lengthcorresponds to a distance of about 78m, which can be obtained even insmall cells. For IS-95 systems, with a chip duration of about 1 µs (i.e about300m), this multipath diversity in small cells is not possible.

• Tracking of phase and amplitude:Within each RAKE finger both, phase and amplitude changes (caused bysmall scale fading) have to be tracked and removed (see Figure C.1).

• Signal Combination:The different signal contributions (the individual RAKE fingers) have to becombined coherently. The resulting symbols can then be presented to thedecoder for further processing.

In order to facilitate the tracking of both, signal-phase and -amplitude, UMTSuses known pilot symbols that are used to sound the channel state (i.e the weight

177

178 APPENDIX C. RAKE RECEPTION

Figure C.1: The principle of maximum ratio combining within the CDMA RAKEreceiver. Source: [54].

vectors) for a particular finger. With this weight vector the received symbol isrotated back in order to cancel the phase rotation caused by the radio propagationchannel. The ”channel-compensated”symbols can then be summed up to recoverthe energy across all delays1.

In Figure C.2 the block diagram of a W-CDMA RAKE receiver is shown. Codegenerators and correlators perform the despreading and integration to user datasymbols of received I- and Q-branches from the HF front-end. The channelestimation uses the pilot symbols for estimating the channel state which willthen be removed by the phase rotator from the received symbols. In the DelayEqualizer the different delays for the individual taps are compensated. Since theindividual taps are uncorrelated (they have different fading statistics), the delayequalization provides a multi-path diversity gain.

In order to perform a successful despreading, code and data timing must beknown. This can be estimated by a so-called matched-filter. A matched filterworks the following way (see Figure C.3): We assume an incoming serial datastream. When the samples of the incoming serial data are equal to bits of prede-fined data (i.e. pilot symbols), there is a maximum at filter output.

Although there are several differences between the UMTS RAKE receivers inthe mobile and the base station [104], all the basic principles presented in thisappendix are the same. To learn more about RAKE reception in UMTS see[54, 97].

1This maximum ratio combining (MRC) algorithm performs optimal in case that the inter-ference is uncorrelated [108].

179

Figure C.2: Block diagram of a W-CDMA RAKE receiver. Source: [54].

Figure C.3: Schematic block diagram of matched-filter.

180 APPENDIX C. RAKE RECEPTION

Appendix D

Rule Set for Rule BasedApproach

The standard rule set for the Rule Based Approach (Section 8.2.1) is shown inTable D.1.



1 tilt 5◦ 5◦ 6


3 tilt 4◦ 5◦ 7


5 tilt 3◦ 6◦ 8


7 tilt 2◦ 7◦ 9


9 tilt 1◦ 8◦ 10

Table D.1: Standard rule set used for Rule Based Approach.

181

182 APPENDIX D. RULE SET FOR RULE BASED APPROACH

Appendix E

Rule Sets for SimulatedAnnealing

The two rule sets, which are used for the Simulated Annealing algorithm (Sec-tion 8.2.2) are listed in the following tables.



1 tilt 5◦ 5◦ 20


3 tilt 4◦ 5◦ 20


5 tilt 3◦ 6◦ 20


7 tilt 2◦ 7◦ 20


9 tilt 1◦ 8◦ 20

10 CPICH -0.5 dB 15 dBm 20

11 tilt 0.5◦ 8◦ 20

Table E.1: Rule set 1 for Simulated Annealing.

183

184 APPENDIX E. RULE SETS FOR SIMULATED ANNEALING



1 tilt 5◦ 5◦ 20


3 tilt 4◦ 5◦ 20


5 tilt 3◦ 6◦ 20


7 tilt 2◦ 7◦ 20


9 tilt 1◦ 8◦ 20

10 CPICH -0.5 dB 15 dBm 20

11 tilt 0.5◦ 8◦ 20

12 CPICH -0.25 dB 14 dBm 20

13 tilt 0.25◦ 8.5◦ 20

Table E.2: Rule set 2 for Simulated Annealing.

Appendix F

Rule Sets for Adaptive RuleBased Approach

The four rule sets, which are used for the Adaptive Rule Based Approach (Sec-tion 8.2.3) are listed in the following four tables.



tilt 1.5◦ 4◦


tilt 1◦ 5◦


tilt 0.5◦ 7◦

Table F.1: Rule set 1 for Adaptive Rule Based Approach.

185

186APPENDIX F. RULE SETS FOR ADAPTIVE RULE BASED APPROACH



tilt 1.5◦ 4◦


tilt 1◦ 5◦


tilt 0.5◦ 6◦


tilt 0.5◦ 7◦




tilt 0.25◦ 4◦


tilt 1.5◦ 4◦


tilt 0.5◦ 5◦


tilt 1.5◦ 5◦


tilt 1◦ 6◦


tilt 1.5◦ 6◦


tilt 0.5◦ 7◦


tilt 1.5◦ 7◦


187



tilt 1.5◦ 4◦


tilt 0.25◦ 4◦


tilt 1.5◦ 5◦


tilt 0.25◦ 5◦


tilt 1.5◦ 6◦


tilt 0.25◦ 6◦


tilt 1.5◦ 7◦


tilt 0.25◦ 7◦


188APPENDIX F. RULE SETS FOR ADAPTIVE RULE BASED APPROACH

Appendix G

Parameter File for GeneticAlgorithm

For the genetic algorithm an XML file is used to specify the most importantparameters. The file contains 3 elements: <ga>, <limit> and <init>. Theelement <ga> specifies the main parameters of the algorithm. The search space isdefined by the element <limit>. The element <init> defines the start parametersettings of the first 12 individuals.

In the element <ga> the attributes define the following:

• pop: Number of individuals in the population.

• cm: Selection pressure Cm.

• local num and local iter: Parameters of the local optimization (Sec-tion 8.3.2.5).

• reduce iter and min pop size: Parameters of Reduced Population Size(Section 8.3.2.7).

• add gos: GoS, where new users are admitted.

• add ever: Parameter of Adding Users as New Impulse (Section 8.3.2.6).

<parameter_file>

<ga

pop="100"

cm="5"

local_num="20"

local_iter="2"

189

190 APPENDIX G. PARAMETER FILE FOR GENETIC ALGORITHM

reduce_iter="999"

min_pop_size="10"

add_gos="0.96"

add_ever="20" >

</ga>

<limit

cpich_upper="38"

cpich_lower="15"

tilt_upper="-2"

tilt_lower="10" >

</limit>

<init>

<param cpich="33" tilt="0" />












</init>

</parameter_file>

Appendix H

Flowcharts for AnalyticOptimization Algorithm

This appendix presents the flowcharts for the Analytic Optimization Algorithmpresented in Section 8.4.

Figure H.1 and Figure H.2 show the implementation of the azimuth adjustmentdescribed in Section 8.4.1. The introduced optimization of the antenna downtiltin Section 8.4.2 is presented by Figure H.3. Figure H.4 and Figure H.5 show theimplementation of the CPICH power adjustment from Section 8.4.3.

The full description of the implementation can be found in Chapter 6 of WolfgangKarner’s diploma thesis [63].

191

192APPENDIX H. FLOWCHARTS FOR ANALYTIC OPTIMIZATION ALGORITHM

Figure H.1: Automatic azimuth adjustment routine for turning base stations tocritical spots.

193

Figure H.2: Automatic azimuth adjustment routine for interleaving base stations.


Figure H.3: Automatic tilt adjustment routine.

195

Figure H.4: Automatic CPICH adjustment routine for CPICH coverage verifica-tion mode 2, function: set CPICH coverage in total scenario.


Figure H.5: Automatic CPICH adjustment routine for CPICH coverage verifica-tion mode 2, function: set CPICH coverage in optimization area.

Appendix I

Simulation Parameters

The following tables summarize the relevant network parameters, which are usedfor CAPESSOTM .

Antenna type Conventional

Antenna height 30m

Antenna sector 65◦

Antenna gain 16 dBi

Antenna pattern Kathrein 739707 (Figure 5.2 and 5.4)

Initial antenna downtilt 0◦ mechanical (varied) + 3◦ predefined electrical (fixed)

Initial antenna azimuth 0◦ (north) / 120◦ / 240◦

Table I.1: Base station antenna parameters in simulator CAPESSOTM .

197

198 APPENDIX I. SIMULATION PARAMETERS

Number of carriers 1

Number of OVSF code trees 1

Maximum base station transmit power 43 dBm

Maximum code power 40 dBm

Minimum code power 15 dBm

Initial CPICH power 33 dBm

Initial SCH power 5 dBm †

Initial PCH power 5 dBm †

Active set window 3dB

Active set size 2

Transmitter loss 0 dB

Base station noise figure 5 dB

Number of RAKE fingers 3

MRC efficiency 1

DL TPC dynamic range 65 dB

DL TPC headroom 1dB

UL TPC dynamic range 25 dB

UL TPC headroom 2dB

†Changed together with CPICH power. See Section 5.3 for details.

Table I.2: System parameters in simulator CAPESSOTM .

199

Background noise floor -107 dBm [82]

Path loss model Macro cell [5], improved COST 259 channel model †

Maximum delay of channel 2 µs

Number of channel taps 5

σ azimuth of taps 5◦

Downlink orthogonality factor 0.4

σ of log normal large scale fading 2 dB

µ of log normal large scale fading 0

Path loss correlation 0.5

ACIR 5%

ISIR 5%

†A detailed description of the COST 259 model is given in Appendix B.

Table I.3: Channel parameters in simulator CAPESSOTM .

Maximum mobile station transmit power 21 dBm

Mobile station antenna gain 0 dB

Body loss 0 dB

Receiver noise figure 0 dB

Receiver sensitivity -120 dBm †

Receiver CPICH threshold -126 dBm

†The Receiver sensitivity is used in CAPESSOTM for setting the EC/I0 threshold in CPICHcoverage verification mode 2 (see Section 7.2.2.2). A receiver sensitivity of -120 dBm is equiva-lent to an EC/I0 threshold of -12 dBm.

Table I.4: Mobile station parameters in simulator CAPESSOTM .

User distribution Best Server Equal / Equal †

Service mix 40% 12.2kbit/s speech, 60% 64kbit/s data

Activity factor 50% speech, 100% data

†See Section 7.4 for a detailed description of the possible user distributions. During this thesisonly the Best Server Equal distribution is used for the evaluation of the different optimizationalgorithms.

Table I.5: User parameters in simulator CAPESSOTM .

200 APPENDIX I. SIMULATION PARAMETERS

Appendix J

Frequently Used Acronyms

2G Second Generation3G Third Generation3GPP Third Generation Partnership ProjectACIR Adjacent Channel Interference RatioACTS Advanced Communications Technologies and ServicesAICH Acquisition Indication ChannelAN Access NetworkAP-AICH Access Preamble Acquisition Indication ChannelARIB Association of Radio Industries and BusinessesAuC Authentication CenterAS Active SetBCH Broadcast ChannelBER Bit Error RateBLER Block Error RateBSC Base Station ControllerBSS Base Station SystemBTS BAse Transceiver Stationcdf cumulative distribution functionCDMA Code Division Multiple Accesscdma2000 3G CDMA standard in USCD/CA-ICH Collision-Detection/Channel-Assignment Indicator ChannelCF Cooling FunctionCN Core NetworkCOST European Cooperation in the field of Scientific and Technical researchCPCH Common Packed ChannelCPICH Common Pilot ChannelCS Circus SwitchedCSICH CPCH Status Indicator ChannelCWTS China Wireless Communication Standard

201

202 APPENDIX J. FREQUENTLY USED ACRONYMS

DCH Dedicated ChannelDECT Digital Enhanced Cordless TelefonDL DownlinkDPCCH Dedicated Physical Control ChannelDPCH Dedicated Physical ChannelDPDCH Dedicated Physical Data ChannelDSCH Downlink Shared ChannelDS-CDMA Direct Sequence CDMADTX Discontinuous transmission modeEA Evolutionary AlgorithmsEIR Equipment Identify RegisterEIRP Equivalent Isotropic Radiated PowerETSI European Telecommunications Standardization InstituteFACH Forward Access ChannelFDD Frequency Division DuplexFDMA Frequency Division Multiple AccessFRAMES Future Radio Wideband Multiple Access SystemGA Genetic AlgorithmGGSN Gateway GPRS Support NodeGoS Grade of ServiceGPRS General Packet Radio ServiceGSM Global System for Mobile communicationsGUI Graphical User InterfaceHCS Hierarchical Cell StructureHF Radio FrequencyHIMM High Interactive MultimediaHLR Home Location RegisterHMM High MultimediaHO HandoverHIPERLAN High Performance Local Area NetworkHTML Hipertext Markup LanguageIF Intermediate FrequencyIMT-2000 International Mobile Communications 2000IP Internet ProtocolISIR Inter System Interference RatioITU International Telecommunications UnionITU-R ITU Radiocommunication sectorLAN Local Access NetworkMDC Macro Diversity CombiningME Mobile EquipmentMMM Medium MultimediaMRC Maximum Ratio CombiningMS Mobile Station

203

MSC Mobile Switching CenterOFDMA Orthogonal Frequency Division Multiplex AccessODMA Opportunity Driven Multiple AccessQAP Quadratic Assignment ProblemQoS Quality of ServiceQPSK Quadrature Phase Shift KeyingOVSF Orthogonal Variable Spreading FactorPCH Paging ChannelPCPCH Physical Common Packed ChannelPDSCH Physical Downlink Shared ChannelPHY PhysicalPICH Paging Indicator ChannelPLMN Public Land Mobile NetworkPN Pseudo NoisePRACH Physical Random Access ChannelPS Packet SwitchedPSD Power Spectrum DensityPSTN Public Switched Telephone NetworkP-CCPCH Primary Common Control Physical ChannelP-CPICH Primary CPICHQF Quality FactorRACE Research of Advanced Communications Technologies in EuropeRACH Random Access ChannelRAN Radio Access NetworkRNC Radio Network ControllerRNS Radio Network SystemRRC Radio Resource ControlRRM Radio Resource ManagementR&D Research and DevelopmentS SpeechSA Simulated AnnealingSCH Synchronization ChannelSD Switched DataSGSN Serving GPRS Support NodeSHO Soft HandoverSIM Subscriber Identity ModuleSIR Signal to Interference RatioSM Simple MessagingSMS Short Message ServiceSNR Signal to Noise RatioS-CCPCH Secondary Common Control Physical ChannelS-CPICH Secondray CPICHT1P1 Technical Subcommittee in US: Wireless/Mobile Services and Systems

204 APPENDIX J. FREQUENTLY USED ACRONYMS

TCP/IP Transmission Control Protocol/ Internet ProtocolTDD Time Division DuplexTDMA Time Division Multiple AccessTD-SCDMA Time Division - Synchronized CDMATFI Transport Format IndicatorTL Tabu ListTPC Transmit Power ControlTS Tabu SearchTSG Technical Specification GroupTSP Traveling Salesman ProblemTTA Telecommunications Technology AssociationTTC Telecommunication Technology CommitteeTX TransmitUE User EquipmentUL UplinkUMTS Universal Mobile Telecommunications SystemUSIM User Service Identity ModuleUTRA UMTS Terrestrial Radio AccessUTRAN UTRA NetworkVLR Visitor Location RegisterWARC World Administrative Radio ConferenceWLAN Wireless LANWLL Wireless Local LoopW-CDMA Wideband CDMAW-TDMA Wideband TDMAWWW World Wide WebXML Extensible Markup Language

Appendix K

Frequently Used Symbols

α Parameter of Geometric Cooling functionαk Orthogonality factor of cell kβ Parameter of Slow Cooling functionβk Scaling factor (relative maximum link powers) for different

base stations in the active setCm Selection Pressurecells Number of cells in the networkCPICHEc/I0 Ec/I0 for the CPICHCPICH EC/I0 thres Threshold for CPICHEc/I0

E Energy stateEb/I0 Bit energy to interference plus noise density ratioEb/N0 Bit energy to noise density ratioEc Average energy per pseudo noise chipEc/I0 Chip energy to interference plus noise density ratioe(i) Number of descendants for individual iexisting Total number of simulated usersηUL Uplink loadingηUL,threshold Maximum cell loadf(i) Scaled fitness value of individual ig(i) Fitness value of individual ig Mean value over all g(i)gmax Highest fitness that occurs in the populationgcov Coverage probability of the pixels in the simulation areaGoS Grade of ServicegSHO SHO proportiongSHO,cell(k) Number of SHO links in cell kgSHO,max Maximum over all gSHO,cell(k)i Other-to-own cell interference ratioI0 Total received power density, including signal and interference,

205

206 APPENDIX K. FREQUENTLY USED SYMBOLS

as measured at the mobile station antenna connectorIACI Adjacent channel interferenceIk Total wideband power received at the mobile station from

base station kIoth Inter-cell interferenceIown Intra-cell interferenceItot Total wideband interference power received at the mobile

stationk Boltzmann’s constantKN Number of mobiles connected to base station NL Interference floorLp Propagation loss between the mobile and the base stationLpi Link loss to base station iLpk

Link loss from base station k to the mobile stationn Population sizeN0 Background noiseNC Number of clustersNMS Background noise level at the mobile stationnumBSs Number of base stations in networkpc Recombination probabilityPcommon Overall transmit power of the common channelsPCPICH CPICH power of best serverPi Total power of ith clusterpm Mutation probabilityPnoise Noise power at 20◦C, which is -108,09 dBmps(i) Selection probability for individual iPT Total required cell’s transmit powerPT,max Maximum cell powerP TX Average code powerPTX Required transmit power for a certain linkPTX,i Total transmit power of base station iPTX,max Maximum base station transmit power capabilityPTX,MS Mobile’s transmit powerPTX,n Required code power for the connected user nQF Quality FactorR Bit rateRk Bit rate of user kRSCPCPICH Received power of the CPICH measured at the mobile stationRSSI Wideband received power within the relevant channel

bandwidth in the downlinkρ Uplink Eb/N0 requirementρk Uplink Eb/N0 requirement of user k

207

S Receiver Sensitivityserved Total number of served usersservedk Number of served users of cell kστ,i Cluster delay spreadT TemperatureTc Chip durationTC Cooling temperatureτi Delay of the first pathν Service activityW W-CDMA chip rate

208 APPENDIX K. FREQUENTLY USED SYMBOLS

Appendix L

Curriculum Vitae

Personal Data

Name: Alexander Gerdenitsch

Address: Berggasse 1, 7022 Loipersbach

Birthday: 21.10.1976, Eisenstadt

Family status: Unmarried

Education

1983-1987 Primary school, 2500 Baden.

1987-1991 Comprehensive secondary school, 2500 Baden.

1991-1996 Federal Secondary College of Engineering Wiener Neustadt,Department of Mechanical Engineering and Automation,School leaving examination in June 1996.

1996-2001 Studies of Mechatronics with focus on Communications and

209

210 APPENDIX L. CURRICULUM VITAE

Information Engineering at Johannes Kepler UniversityLinz to achieve the Diplom-Ingenieur (Master degree).

2002-2004 PhD studies of Electrical and Electronical Engineering atthe Institute of Communications and Radio-FrequencyEngineering.

Military service

04/2001 - 11/2001 Completion of the military service in the Austrian army.

Career

12/2001 - 6/2002 Technical employee in the software department of thecompany Landis & Gyr, 7000 Eisenstadt.

Since 7/2002 Research engineer at the Institute of Communicationsand Radio-Frequency Engineering, Gußhausstrae 25/389,1040 Wien.

Publications

• S. Jakl, A. Gerdenitsch, W. Karner, M. Toeltsch, ”An Approach for theInitial Adjustment of Antenna Azimuth and Other Parameters in UMTSNetworks”, Proc. 13th IST Mobile & Wireless Communications Summit2004, June 2004, Lyon, France.

• A. Gerdenitsch, S. Jakl, W. Karner, M. Toeltsch, ”Influence of AntennaAzimuth in Non-Regular UMTS Networks”, Proc. 5th World WirelessCongress, San Francisco, US, 2004.

• A. Gerdenitsch, S. Jakl, M. Toeltsch, ”The Use of Genetic Algorithms forCapacity Optimization in UMTS FDD Networks”, Proc. 3rd InternationalConference on Networking ICN’04, vol. 1, pp. 293-298, ISBN: 0-86341-326-9, Guadeloupe, French Caribbean, 2004.

211

• A. Gerdenitsch, S. Jakl, Y.Y. Chong, M. Toeltsch, ”An Adaptive Algorithmfor CPICH and Antenna Tilt Optimization in UMTS FDD Networks”, Proc.8th International Conference on Cellular and Intelligent Communications(CIC), p. 378, ISBN: 89-5519-118-9-98560, October 2003, Seoul, Korea.

• A. Gerdenitsch, S. Jakl, M. Toeltsch, T. Neubauer, ”Intelligent Algorithmsfor System Capacity Optimization of UMTS FDD Networks”, Proc. IEE4th International Conference on 3G Mobile Communication Technologies,pp. 222-226, ISBN: 0-85296 756-X, June 2003, London.

• A. Springer, A. Gerdenitsch, Z. Li, A. Stelzer, R. Weigel, ”Adaptive Predis-tortion for Amplifier Linearization for UMTS Terminals”, Proc. IEEE 7th

International Symposium on Spread-Spectrum Techniques and Applications,pp. 78-82, ISBN: 0-7803-7627-7, Sept. 2002, Prague.

• A. Springer, A. Gerdenitsch, R. Weigel, ”Digital Predistortion-Based PowerAmplifier Linearization for UMTS”, Proc. European Conference on Wire-less Technology (ECWT2001), pp. 185-189, ISBN: 0 86213 163 4, Sept.2001, London.

• Alexander Gerdenitsch, ”Digitale Vorverzerrung zur Linearisierung von Leis-tungsverstarkern fur UMTS”, Master Thesis, June 2001.

212 APPENDIX L. CURRICULUM VITAE

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