Ulrich Türke

Efficient Methods for WCDMA Radio Network Planning and Optimization

TEUBNER RESEARCH

Advanced Studies Mobile Research Center BremenHerausgeber/Editors:Prof. Dr. Otthein Herzog Prof. Dr. Carmelita Görg Prof. Dr.-Ing. Bernd Scholz-Reiter Dr. Ulrich Glotzbach

Das Mobile Research Center Bremen (MRC) erforscht, entwickelt underprobt in enger Zusammenarbeit mit der Wirtschaft mobile Informatik-,Informations- und Kommunikationstechnologien. Als Forschungs- undTransferinstitut des Landes Bremen vernetzt und koordiniert das MRChochschulübergreifend eine Vielzahl von Arbeitsgruppen, die sich mitder Entwicklung und Anwendung mobiler Lösungen beschäftigen. DieReihe „Advanced Studies“ präsentiert ausgewählte hervorragendeArbeitsergebnisse aus der Forschungstätigkeit der Mitglieder des MRC.

In close collaboration with the industry, the Mobile Research CenterBremen (MRC) investigates, develops and tests mobile computing,information and communication technologies. This research associationfrom the state of Bremen links together and coordinates a multiplicity ofresearch teams from different universities and institutions, which areconcerned with the development and application of mobile solutions.The series “Advanced Studies“ presents a selection of outstandingresults of MRC’s research projects.

Ulrich Türke

Efficient Methods forWCDMA Radio NetworkPlanning and Optimization

TEUBNER RESEARCH

Bibliographic information published by Die Deutsche NationalbibliothekDie Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie;detailed bibliographic data is available in the Internet at <http://dnb.d-nb.de>.

Dissertation Universität Bremen, 2007

1st Edition September 2007

All rights reserved© Deutscher Universitäts-Verlag | GWV Fachverlage GmbH, Wiesbaden 2007

Readers: Ute Wrasmann / Anita Wilke

Deutscher Universitäts-Verlag and Teubner Verlag are companies of Springer Science+Business Media.www.duv.de www.teubner.de

No part of this publication may be reproduced, stored in a retrieval systemor transmitted, mechanical, photocopying or otherwise without priorpermission of the copyright holder.

Registered and/or industrial names, trade names, trade descriptions etc. cited in this publica-tion are part of the law for trade-mark protection and may not be used free in any form or byany means even if this is not specifically marked.

Cover design: Regine Zimmer, Dipl.-Designerin, Frankfurt/MainPrinted on acid-free paperPrinted in Germany

ISBN 978-3-8350-0903-5

Gedruckt mit freundlicher Unterstützung des MRC Mobile Research Center der Universität Bremen

Printed with friendly support of MRC Mobile Research Center, Universität Bremen

Preface

When joining Siemens in 2001, I also extended my research interest towards radio net-work planning methodologies. This area of research brought together my personal interestin mobile communications and in the design of efficient algorithms and data structures.Between 2001 and 2003, I participated in the EU project Momentum, which was target-ing the performance evaluation and optimization of UMTS radio networks. In this projectmy main focus was on Monte-Carlo snapshot techniques, shortly after I broadened myresearch scope towards network optimization techniques. Both constitute the basis of thisthesis. Later I got the chance to bring in the ideas and methods of my thesis into a Siemensinternal planning and optimization tool, the development of which I guided over the lasttwo years.

First of all I would like to thank Professor Dr. Carmelita Görg for supervising this thesis,for her guidance, and her encouragement. I very much appreciate that she - although I,as an external Ph.D. student, was not part of her department at the University of Bremen- was always very open for discussions and provided me with a large amount of usefulsuggestions over the past years. I without a doubt greatly benefited from this, both in myscientific work and personally. I am also deeply grateful for the comments and suggestionsby Professor Dr. Thomas Kürner, who is also the second examiner of this thesis. Thediscussions with him and his advice - in particular in the last phase of the work on thisthesis - were of great help to me. I am deeply obliged to Helmut Mühlbauer for giving methe chance to conduct this thesis within Siemens, for his strong faith in me and my work,and for strengthening my view on economical aspects and the practical relevance of myresearch. Furthermore I am very grateful to Professor Dr. Ranjit Perera, who participatedas a guest scientist in the EU project Momentum. I very much appreciated the long andfruitful discussions with him - not only on technical topics. Special thanks go to mycolleagues Michael Koonert and Richard Schelb for many discussions, and not least forproof-reading parts of this thesis.

Ulrich Türke

Abstract

The planning of mobile radio networks requires fast and accurate methods for networkperformance evaluation. In this thesis novel methods for the performance analysis ofWCDMA networks are developed and discussed.

The state-of-the-art approach for evaluating the performance of WCDMA networks is theMonte-Carlo snapshot simulation. However, current solutions based on snapshot simu-lations have major weaknesses with respect to accuracy, simulation time, and flexibility.In particular the size of networks that can be analyzed in an acceptable time frame withstandard hardware resources is typically very limited.

The new snapshot analysis method developed in this thesis significantly outperforms thestate-of-the-art methods. It allows for a detailed analysis of very large networks (N x 1000base station sites) on a standard PC within running times acceptable for everyday plan-ning. This is mainly achieved by a highly linear modeling of the system and by a reductionof the size of the sets of linear equations to be calculated in each snapshot. The dimensionreduction is achieved by the application of a cell based transmission power and interfer-ence level calculation as opposed to the standard mobile based approach. Despite thesignificant speed-up achieved in the evaluation of a snapshot, the proposed approach alsoallows a very accurate and flexible modeling of system features, e.g. with respect to RadioResource Management (RRM) and Quality of Service (QoS).

Increasing the performance of evaluating a single snapshot is only one approach to reducethe running time of the snapshot analysis. Besides this, also the number of snapshots re-quired to yield a certain accuracy of results can be reduced by means of variance reductiontechniques. In the area of snapshot simulations for network planning, variance reductiontechniques have almost not been considered yet. In this thesis considerable effort has beenput into evaluating the applicability of variance reduction techniques for cutting down therunning time of the snapshot analysis. Based on the discussion of several variance re-duction techniques known from theory, two variance reduction techniques are identifiedand tailored to an application in everyday planning. Both, control variates and correlatedsampling, are applied to example planning scenarios. Considerable gains with respect tothe number of required snapshots to yield a certain accuracy of results are obtained forthe proposed variance reduction methods.

viii Abstract

While providing very accurate results at a high level of detail, for some applications thesnapshot simulation approach is too time consuming. In particular search based opti-mization techniques, which compare a large number of different network configurations,require methods with much shorter running times, while accepting a loss in level of detailand accuracy. Targeting such areas the analytical methods developed in this thesis canbe effectively applied. Based on a static method two novel extended statistical methods,incorporating shadow fading statistics, are proposed. Results are presented showing sig-nificant improvements in approximating the results from snapshot simulations in terms ofrelevant system parameters. Finally, the application of the developed methods in networkoptimization is discussed. Two different optimization methods are developed. The pre-sented results prove the value of the methods for an application in network optimization.

The majority of the developed methods is currently successfully applied in a radio net-work planning tool.

Contents

List of Abbreviations xiii

List of Symbols xvii

List of Figures xxi

List of Tables xxv

1 Introduction 1

2 The WCDMA Air Interface 52.1 UMTS Network Architecture . . . . . . . . . . . . . . . . . . . . . . . . 62.2 UTRA-FDD Protocols . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.2.1 Physical Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.2.2 Data Link Layer . . . . . . . . . . . . . . . . . . . . . . . . . . 142.2.3 Network Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . 162.2.4 High Speed Packet Access Extensions . . . . . . . . . . . . . . . 17

2.3 Radio Resource Management . . . . . . . . . . . . . . . . . . . . . . . . 192.3.1 Soft Handover . . . . . . . . . . . . . . . . . . . . . . . . . . . 202.3.2 Power Control . . . . . . . . . . . . . . . . . . . . . . . . . . . 222.3.3 Admission Control . . . . . . . . . . . . . . . . . . . . . . . . . 232.3.4 Congestion Control . . . . . . . . . . . . . . . . . . . . . . . . . 23

3 Modeling the Wireless Transmission Channel 253.1 Macro Path Loss Prediction . . . . . . . . . . . . . . . . . . . . . . . . . 26

3.1.1 Basic Propagation Models . . . . . . . . . . . . . . . . . . . . . 263.1.2 Empirical Prediction Models . . . . . . . . . . . . . . . . . . . . 283.1.3 Improved Models . . . . . . . . . . . . . . . . . . . . . . . . . . 30

3.2 Shadow Fading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313.2.1 Modeling of Spatial Correlation . . . . . . . . . . . . . . . . . . 323.2.2 Modeling of Link Correlation . . . . . . . . . . . . . . . . . . . 333.2.3 Implementation in Simulations . . . . . . . . . . . . . . . . . . . 34

3.3 Fast Fading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353.4 Antenna Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363.5 Signal and Interference Levels . . . . . . . . . . . . . . . . . . . . . . . 39

x Contents

3.5.1 Effective Interference Raise . . . . . . . . . . . . . . . . . . . . 423.5.2 Power Control Headroom . . . . . . . . . . . . . . . . . . . . . 433.5.3 Soft-Handover Gain . . . . . . . . . . . . . . . . . . . . . . . . 43

4 Monte-Carlo Snapshot Analysis 454.1 The Monte-Carlo Method . . . . . . . . . . . . . . . . . . . . . . . . . . 46

4.1.1 General Concept . . . . . . . . . . . . . . . . . . . . . . . . . . 464.1.2 Convergence Estimation . . . . . . . . . . . . . . . . . . . . . . 47

4.2 Simple Analysis Example . . . . . . . . . . . . . . . . . . . . . . . . . . 494.3 Variance Reduction Techniques . . . . . . . . . . . . . . . . . . . . . . . 55

4.3.1 Importance Sampling . . . . . . . . . . . . . . . . . . . . . . . . 564.3.2 Stratified Sampling . . . . . . . . . . . . . . . . . . . . . . . . . 604.3.3 Control Variates . . . . . . . . . . . . . . . . . . . . . . . . . . 614.3.4 Correlated Sampling . . . . . . . . . . . . . . . . . . . . . . . . 63

4.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

5 Snapshot Analysis for WCDMA Network Performance Evaluation 675.1 Basic Analysis Loop . . . . . . . . . . . . . . . . . . . . . . . . . . . . 675.2 Snapshot Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 685.3 Modeling of Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . 705.4 System Constraints and Limits . . . . . . . . . . . . . . . . . . . . . . . 715.5 Cell Based Radio Performance Evaluation . . . . . . . . . . . . . . . . . 73

5.5.1 Uplink Equations . . . . . . . . . . . . . . . . . . . . . . . . . . 755.5.2 Downlink Equations . . . . . . . . . . . . . . . . . . . . . . . . 775.5.3 Soft Handover . . . . . . . . . . . . . . . . . . . . . . . . . . . 795.5.4 Iterative Solution . . . . . . . . . . . . . . . . . . . . . . . . . . 79

5.6 Resource Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 815.6.1 QoS Concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . 825.6.2 Analysis Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 835.6.3 Application Example . . . . . . . . . . . . . . . . . . . . . . . 89

5.7 Method Extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 925.7.1 Multi Carrier Analysis . . . . . . . . . . . . . . . . . . . . . . . 925.7.2 HSPA Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

5.8 Application of Variance Reduction Techniques . . . . . . . . . . . . . . 935.8.1 Control Variates . . . . . . . . . . . . . . . . . . . . . . . . . . 945.8.2 Correlated Sampling . . . . . . . . . . . . . . . . . . . . . . . . 102

5.9 Validation of Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1035.9.1 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

5.10 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

6 Analytical Performance Evaluation 1076.1 Static Load Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

6.1.1 Average Required Link Power . . . . . . . . . . . . . . . . . . . 108

Contents xi

6.1.2 Cell Assignment . . . . . . . . . . . . . . . . . . . . . . . . . . 1116.1.3 Serving Probability . . . . . . . . . . . . . . . . . . . . . . . . . 1116.1.4 Coverage Probability . . . . . . . . . . . . . . . . . . . . . . . . 1116.1.5 Characteristic Cell Power Equations . . . . . . . . . . . . . . . . 1116.1.6 Application Example: Static Load Estimation . . . . . . . . . . . 113

6.2 Statistical Load Estimation . . . . . . . . . . . . . . . . . . . . . . . . . 1166.2.1 Serving Probabilities . . . . . . . . . . . . . . . . . . . . . . . . 1166.2.2 Coverage Probability . . . . . . . . . . . . . . . . . . . . . . . . 1176.2.3 Characteristic Cell Power Equations . . . . . . . . . . . . . . . . 1186.2.4 Application Example: Statistical Load Estimation . . . . . . . . . 119

6.3 Extended Statistical Load Estimation . . . . . . . . . . . . . . . . . . . . 1206.3.1 Average Required Link Power . . . . . . . . . . . . . . . . . . . 1206.3.2 Characteristic Cell Power Equations . . . . . . . . . . . . . . . . 1256.3.3 Application: Extended Statistical Load Estimation . . . . . . . . 126

6.4 Evaluation of Per Pixel Quantities . . . . . . . . . . . . . . . . . . . . . 1276.4.1 Pilot RSCP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1286.4.2 Soft-Handover Probability . . . . . . . . . . . . . . . . . . . . . 1306.4.3 Pilot Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1316.4.4 Downlink Connection Power . . . . . . . . . . . . . . . . . . . . 1336.4.5 Uplink Connection Power . . . . . . . . . . . . . . . . . . . . . 135

6.5 Practical Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . 1356.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136

7 Automated Network Optimization 1377.1 Optimization Parameters and Targets . . . . . . . . . . . . . . . . . . . . 1377.2 Optimization based on Fast Heuristic . . . . . . . . . . . . . . . . . . . 1387.3 Search Based Optimization . . . . . . . . . . . . . . . . . . . . . . . . . 142

7.3.1 A Brief Introduction to Local Search . . . . . . . . . . . . . . . . 1427.3.2 Application of Local Search to Radio Network Planning . . . . . 1457.3.3 Advanced Search Algorithm . . . . . . . . . . . . . . . . . . . . 147

7.4 Application Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1507.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152

8 Conclusions and Outlook 155

A Generating Correlated Random Variables 157

Bibliography 159

List of Abbreviations

3GPP 3rd Generation Partnership ProjectAAP Analysis of Adjacent PointsAC Admission ControlAICH Acquisition Indication ChannelAMC Adaptive Modulation and CodingAMR Adaptive Multi–RateARIB Association of Radio Industries and BusinessesARPU Average Revenue Per SubscriberARQ Automatic Repeat RequestATIS Alliance for Telecommunications Industry SolutionsBCCH Broadcast Control ChannelBCH Broadcast ChannelBHCA Busy Hour Call AttempsBLER Block Error RateBMC Broadcast/Multicast Control ProtocolBPSK Binary Phase Shift KeyingBS Base StationC-Plane Control PlaneCC Congestion ControlCCCH Common Control ChannelCCSA China Communications Standards AssociationCDMA Code Division Multiple AccessCIR Carrier to Interference plus Noise RatioCN Core NetworkCorS Correlated SamplingCPCH Uplink Common Packet ChannelCPICH Common Pilot ChannelCS Circuit-SwitchedCTCH Common Traffic ChannelCV Control VariatesD-AMPS Digital Advanced Mobile Phone SystemDCCH Dedicated Control ChannelDCH Dedicated Transport ChannelDL Downlink

xiv List of Abbreviations

DPCCH Dedicated Physical Control ChannelDPDCH Dedicated Physical Data ChannelDS-CDMA Direct Sequence Code Division Multiple AccessDSCH Downlink Shared ChannelDTCH Dedicated Traffic ChannelDTX Discontinuous TransmissionE-AGCH E-DCH Absolute Grant ChannelE-DCH Enhanced Dedicated ChannelE-DPCCH Enhanced Dedicated Physical Control ChannelE-DPDCH Enhanced Dedicated Physical Data ChannelE-HICH HARQ Indicator ChannelE-RGCH E-DCH Relative Grant ChannelEGSM Extended Global System for Mobile CommunicationsETSI European Telecommunications Standards InstituteEU European UnionFACH Forward Access ChannelFDD Frequency Division DuplexFDMA Frequency Division Multiple AccessGGSN Gateway GPRS Support NodeGMSC Gateway MSCGSM Global System for Mobile CommunicationsHARQ Hybrid Automatic Repeat RequestHCS Hybrid Coefficient ScalingHLR Home Location RegisterHPI Horizontal Projection InterpolationHS-DSCH Highspeed Downlink Shared ChannelHS-PDSCH High-Speed Physical Downlink Shared ChannelHS-DPCCH High-Speed Dedicated Physical Control ChannelHSDPA High Speed Downlink Packet AccessHSPA High Speed Packet AccessHSUPA High Speed Uplink Packet AccessIIR Infinite Impulse ResponseIMT2000 International Mobile Telecommunications-2000IP Internet ProtocolIS Importance SamplingISDN Integrated Services Digital NetworkISI Inter-Symbol InterferenceITU International Telecommunications UnionLOS Line of SightLTE (3GPP) Long Term EvolutionMAC Medium Access ControlME Mobile EquipmentMS Mobile Station

List of Abbreviations xv

MSC Mobile Services Switching CenterNCS Noise Coefficient ScalingNLOS Non Line of SightNOF Maximum Neighbor Offset FactorOCCS Own Cell Coefficient ScalingPBDG Path loss Based DowngradingPC Personal ComputerPCCH Paging Control ChannelPCCPCH Primary Common Control Physical ChannelPCH Paging ChannelPCPCH Physical Common Packet ChannelPDC Personal Digital CellularPDCP Packet Data Convergence ProtocolPICH Paging Indication ChannelPLMN Public Land Mobile NetworkPRACH Physical Random Access ChannelPS Packet-SwitchedPSCH Physical Downlink Shared ChannelPSTN Public Switched Telephone NetworkPHY Physical LayerQoS Quality of ServiceQPSK Quadrature Phase Shift KeyingRACH Random Access ChannelRDG Random DowngradingRLC Radio Link ControlRNC Radio Network ControllerRNS Radio Network Sub-SystemROI Return on InvestmentRRC Radio Resource ControlRRM Radio Resource ManagementRSCP Received Signal Code PowerSCH Synchronization ChannelSCCPCH Secondary Common Control Physical ChannelSF Spreading FactorSGSN Serving GPRS Support NodeSHO Soft-HandoverSNR Signal to Interference plus Noise RatioSTD Short-Term Dynamic SimulationStrS Stratified SamplingTCP Transmission Control ProtocolTD-SCDMA Time Division-Synchronous Code Division Multiple AccessTDD Time Division DuplexTDMA Time Division Multiple Access

xvi List of Abbreviations

TTA Telecommunications Technology AssociationTTC Telecommunication Technology CommitteeTTI Transmission Time IntervalTX TransmitU-Plane User PlaneUE User EquipmentUL UplinkUMTS Universal Mobile Telecommunications SystemURA UTRAN Registration AreaUSIM UMTS Subscriber Identity ModuleUTRA Universal Terrestrial Radio AccessUTRAN Universal Terrestrial Radio Access NetworkVLR Visitor Location RegisterWBI Weighted Bilinear InterpolationWCDMA Wideband Code Division Multiple AccessWiMAX Worldwide Interoperability for Microwave Access

List of Symbols

∆SHO SHO margin

Λij Fast fading contribution to link gain between i and j in decibel

Λij Fast fading contribution to link gain between i and j in linear scale

Φ (x) Standard normal distribution

Ω Antenna gain

ΩG Global antenna gain

ΩH() Contribution of horizontal antenna pattern to total antenna gain

ΩV () Contribution of vertical antenna pattern to total antenna gain

Σij Shadow fading contribution to link gain between i and j in decibel

Θij Total path loss between i and j

αi Orthogonality factor applicable for mobile/pixel i

β Scaling constant (control variate constant)

γ↑i Required Uplink (UL) signal to interference plus noise ratio

γ↓i Required Downlink (DL) signal to interference plus noise ratio

γi, min Minimum required pilot signal to total interference plus noise ratio

δi,p,s Serving probability of cell i at pixel p for service s

δSHOi,p,s SHO probability of best serving cell i at pixel p for service s

δSHOp,s Total SHO probability at pixel p for service s

ε↑i UL source activity factor for mobile/pixel i

ε↓i DL source activity factor for mobile/pixel i

ζ↓ij DL link coefficient

ζ∗↓i,j,p Mean DL link coefficient

η↑i Total noise perceived by cell i

η↓i Total noise perceived at mobile/pixel i

θp,s User density of service s in pixel p

κ↑ij Effective UL interference raise between mobile/pixel i and cell j

xviii List of Symbols

κ↓ij Effective DL interference raise between cell i and mobile/pixel j

ρL Shadow fading correlation factor

λ Wave length

λ Arrival Rate

µX Mean value of X , µX = E X

ν↓i DL link inhomogeneity of mobile/pixel i

ν∗↓p,s Mean DL link inhomogeneity of pixel p for service s

ψ Variance reduction coefficient

σ Shadow fading deviation

σ′p Location specific shadow fading deviation

σ′′cp Link specific shadow fading deviation

σX Standard deviation of X

υi,p,s Coverage probability of best serving cell i at pixel p for service s

φ (x) Standard normal density function

τc Total power for all common channels of cell c (static cell power)

τc,s Total power for all common channels on scrambling code s of cell c

~τc Vector of common channel power levels for each scrambling code of cell c

τc Pilot channel power of cell c

τc Static channel power of cell c without pilot channel power: τc = τc − τc

ξ↑ij Total UL link gain between mobile/pixel i and cell j

ξ↓ij Total DL link gain between cell i and mobile/pixel j

ξ∗↓i,p,s Median DL link gain between cell i and pixel p for service s

ξij Time varying link gain between transmitter i and receiver j (subject to fastfading)

ωip Shadow fading component between cell c and pixel p

B User bit rate

C Set of all cells in the system

C Control variate

CIR Carrier to interference ratio

CIR Time varying carrier to interference ratio (subject to fast fading)

E X Expected value of XEb

N0(Required) bit-energy to interference plus noise ratio

List of Symbols xix

Ec

I0Received pilot power to total interference ratio plus noise ratio

I Identity matrix

M Set of served mobiles/users

Mc Set of mobiles/users served by primary cell c

Mc,s Set of mobiles/users served by scrambling code s of primary cell c

P i Probability of a certain event i

PG CDMA processing gain

RpSp Best serving cell at pixel p (random variable)

R∗p Static best serving cell at pixel p

Sp Serving set at pixel p

T ↑ Characteristic UL coupling matrix

T ↓ Characteristic DL coupling matrix

T ∗↓ Mean characteristic DL coupling matrix

W Chip-rate (UMTS: 3.84 · 106)

c Speed of Light

cov X, Y Covariance of X and Y

e Mean absolute error

f Frequency

hBS BS height

hMS MS height

hX(x) Probability density function of random variable X

h↑c Characteristic UL inhomogeneity of cell c

h↓c Characteristic DL inhomogeneity of cell c

h∗c↓ Mean characteristic DL inhomogeneity of cell c

~h∗↓ Mean vector of characteristic DL inhomogeneities

~h↑ Vector of characteristic UL inhomogeneities per cell

~h↓ Vector of characteristic DL inhomogeneities per cell

~h∗↓ Vector of mean characteristic DL inhomogeneities per cell

i↑c Total interference at cell c

i↑c UL other system interference level at cell c

~i Vector of all cell interference levels i↑c

ii Time varying received interference at receiver i (subject to fast fading)

xx List of Symbols

i↓i Total interference at mobile/pixel i

i↓i DL other system interference at mobile/pixel i

l↑c UL cell load of cell c

l↓c DL cell load of cell c

l↑c, max Maximum allowed UL cell load of cell c

l↓c, max Maximum allowed DL cell load of cell c

p↓c Total output power of cell c

p∗c↓ Mean total output power of cell c

~p Vector of total cell output power levels

~p Mean vector of total cell output power levels

p↓c, max Maximum output power of cell c

~p Vector of total cell power levels p↓c

r Correlation coefficient

r↑ci Received UL link power from mobile/pixel i at cell c

r↓jc Received DL link power from cell c at mobile/pixel i

ri Received pilot power of primary serving cell at mobile/pixel i

ri, min Minimum required pilot power at mobile/pixel i

ri Time varying received power of link i (subject to fast fading)

SF i Spreading factor of channel i

Sc Code allocation matrix of cell c

Scij Element (i,j) of the code allocation matrix Sc

t↑i Output power of mobile/pixel i

t↑i, min Minimum output power of mobile/pixel i

t↑i, max Maximum output power of mobile/pixel i

t↓i DL transmit power to mobile/pixel i

t∗↓i,p,s Mean DL transmit power required by serving cell c for pixel p and service s

t↓i, max Maximum DL transmit power to mobile/pixel i

ti Time varying transmit power of transmitter i (subject to fast fading)

var X Variance of random variable X

vc,s Total output power of scrambling code s of cell c

~vc Vector of output powers vc,s for each scrambling code s of cell c

List of Figures

2.1 Basic architecture of a UMTS network . . . . . . . . . . . . . . . . . . . 72.2 UTRAN protocol stack . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.3 UTRAN transport channel to physical channel mapping (3GPP Release 99) 102.4 Tree of orthogonal spreading codes . . . . . . . . . . . . . . . . . . . . . 122.5 UTRAN logical channels to transport channel mapping (3GPP Release 99) 152.6 UE Modes and RRC states in connected mode . . . . . . . . . . . . . . . 172.7 Illustration of the WCDMA soft handover algorithm . . . . . . . . . . . 21

3.1 Radio propagation channel . . . . . . . . . . . . . . . . . . . . . . . . . 253.2 Considered constellation for plane earth loss . . . . . . . . . . . . . . . . 283.3 Illustration of the notation used for describing shadowing . . . . . . . . . 313.4 Generation of spatially correlated shadowing values . . . . . . . . . . . . 323.5 Three-dimensional antenna pattern . . . . . . . . . . . . . . . . . . . . . 373.6 Horizontal and vertical antenna pattern of example antenna . . . . . . . . 383.7 Modified vertical pattern due to multi-path effects . . . . . . . . . . . . . 39

4.1 Simple one cell scenario . . . . . . . . . . . . . . . . . . . . . . . . . . 494.2 Mean cell transmission power vs. offered traffic . . . . . . . . . . . . . . 524.3 Blocking probability vs. offered traffic . . . . . . . . . . . . . . . . . . . 524.4 Mean power: Estimated and observed rate of convergence . . . . . . . . . 534.5 Required snapshots vs. offered traffic - estimation of mean total cell power 534.6 Required snapshots as a function of the estimated mean total cell power . 544.7 Required snapshots vs. offered traffic - estimation of blocking probability 544.8 Required snapshots as a function of the estimated blocking probability . . 554.9 Correlation between total cell TX power and number of users for the sim-

ple analysis example . . . . . . . . . . . . . . . . . . . . . . . . . . . . 584.10 Application of importance sampling: Original distribution functions hX(n),

cell TX power estimate g(n), and modified distribution function hY (n) . . 594.11 Variance reduction in estimating the mean TX power applying importance

sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 604.12 Variance reduction in estimating the blocking probability applying impor-

tance sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 604.13 Variance reduction in estimating mean cell TX power applying control

variates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

xxii List of Figures

4.14 Variance reduction in estimating blocking probabilities applying controlvariates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

4.15 Variance reduction in estimating the difference in cell TX power for dif-ferent changes in BS antenna height applying correlated sampling . . . . 65

4.16 Variance reduction in estimating the difference in blocking probability fordifferent changes in BS antenna height applying correlated sampling . . . 65

5.1 Basic snapshot simulation process . . . . . . . . . . . . . . . . . . . . . 685.2 Modeling of CS and PS traffic sources . . . . . . . . . . . . . . . . . . . 685.3 Snapshot generation process . . . . . . . . . . . . . . . . . . . . . . . . 695.4 Rate of convergence for different iterative load calculation algorithms . . 805.5 Typical number of required iterations per snapshot to yield a relative error

of less than 1% . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 815.6 CS state diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 845.7 PS state diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 845.8 Snapshot evaluation algorithm . . . . . . . . . . . . . . . . . . . . . . . 865.9 Resource scheduling algorithm . . . . . . . . . . . . . . . . . . . . . . . 885.10 Considered hexagonal scenario . . . . . . . . . . . . . . . . . . . . . . . 905.11 Average DL user throughput for different services and resource allocation

schemes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 915.12 Relative increase in capacity applying a path loss based downgrading

(PBDG) compared with random downgrading (RDG) . . . . . . . . . . . 915.13 Correlation between control variate and results from snapshot analysis . . 965.14 Variance reduction in estimating the mean cell TX power achieved by

applying control variates (optimum β) . . . . . . . . . . . . . . . . . . . 1005.15 Variance reduction in estimating the blocking probability achieved by ap-

plying control variates (optimum β) . . . . . . . . . . . . . . . . . . . . 1015.16 Variance reduction in estimation of the mean DL network throughput ap-

plying correlated sampling . . . . . . . . . . . . . . . . . . . . . . . . . 103

6.1 Realistic planning scenario considered in this chapter . . . . . . . . . . . 1136.2 Correlation between the mean cell TX power obtained from the static

method and snapshot analysis for different traffic types . . . . . . . . . . 1146.3 Correlation coefficient and mean absolute error in cell power estimation

for the static method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1156.4 Correlation between the mean cell TX power obtained from basic statis-

tical method and snapshot analysis for different traffic types . . . . . . . . 1196.5 Correlation coefficient and mean absolute error in cell power estimation

for the basic statistical method . . . . . . . . . . . . . . . . . . . . . . . 1206.6 Correlation between the mean cell TX power obtained from extended sta-

tistical load estimation and snapshot analysis for different traffic types . . 1266.7 Correlation coefficient and mean absolute error in cell power estimation

for the extended statistical load estimation . . . . . . . . . . . . . . . . . 127

List of Figures xxiii

6.8 Evaluation loop to be carried out for detailed load and outage analysis . . 1276.9 Shadowing gain considering three potential servers . . . . . . . . . . . . 1296.10 Shadowing gain as a function of the number of potential servers . . . . . 1296.11 Comparison of the error in estimating mean Ec/I0 and probability of Ec/I0

below threshold per pixel . . . . . . . . . . . . . . . . . . . . . . . . . . 1326.12 Comparison of the error in estimating mean DL connection power and

DL connection power outage per pixel . . . . . . . . . . . . . . . . . . . 1336.13 Error and correlation in estimating overall outage based on the static and

the statistical analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134

7.1 Illustration of “ray analysis” . . . . . . . . . . . . . . . . . . . . . . . . 1407.2 Results of the pilot power optimization for different settings of NOF . . . 1417.3 Example application of fast heuristics for azimuth optimization . . . . . . 1437.4 Basic local search algorithm . . . . . . . . . . . . . . . . . . . . . . . . 1467.5 Illustration of the neighborhood tree . . . . . . . . . . . . . . . . . . . . 1487.6 Hybrid local search algorithm . . . . . . . . . . . . . . . . . . . . . . . 1497.7 Comparison of missed traffic per reason before and after optimization . . 1507.8 Comparison of percentage of cells with more than 2% missed traffic be-

fore and after optimization . . . . . . . . . . . . . . . . . . . . . . . . . 1517.9 Comparison of average cell load before and after optimization . . . . . . 1517.10 Comparison of SHO probability before and after optimization . . . . . . 1527.11 Graphical illustration of the probability of missed traffic for the original

network compared with three optimized networks . . . . . . . . . . . . . 153

List of Tables

3.1 Applicability of empirical path loss models . . . . . . . . . . . . . . . . 303.2 Typical shadow fading parameters . . . . . . . . . . . . . . . . . . . . . 35

4.1 Parameters and parameter settings: Simple Example . . . . . . . . . . . . 51

5.1 Bearer table for a speech service . . . . . . . . . . . . . . . . . . . . . . 825.2 Bearer table for a PS data service . . . . . . . . . . . . . . . . . . . . . . 835.3 Cost allocation table for the three used PS services . . . . . . . . . . . . 905.4 Considered traffic mix . . . . . . . . . . . . . . . . . . . . . . . . . . . 955.5 Variance reduction in estimating the mean cell TX power applying control

variates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1005.6 Variance reduction in estimating the blocking probability applying control

variates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1015.7 Variance reduction in estimation of the mean DL network throughput ap-

plying correlated sampling . . . . . . . . . . . . . . . . . . . . . . . . . 102

6.1 Traffic parameters used for example results in this chapter . . . . . . . . . 1136.2 Shadow fading parameters used for example results in this chapter . . . . 1146.3 Means for evaluating the most important quantities per pixel . . . . . . . 135