Efficient Methods for WCDMA Radio Network Planning and Optimization

186 Pages·2007·2.435 MB·English

by  Ulrich Türke (auth.)

Ulrich Türke
Efficient Methods for WCDMA Radio Network Planning
and Optimization
TEUBNER RESEARCH
Advanced Studies Mobile Research Center Bremen
Herausgeber/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 und
erprobt in engerZusammenarbeit mit derWirtschaft mobile Informatik-,
Informations- und Kommunikationstechnologien. Als Forschungs- und
Transferinstitut des Landes Bremen vernetzt und koordiniert das MRC
hochschulübergreifend eine Vielzahl von Arbeitsgruppen, die sich mit
der Entwicklung und Anwendung mobiler Lösungen beschäftigen. Die
Reihe „Advanced Studies“ präsentiert ausgewählte hervorragende
Arbeitsergebnisse aus der Forschungstätigkeit der Mitglieder des MRC.
In close collaboration with the industry, the Mobile Research Center
Bremen (MRC) investigates, develops and tests mobile computing,
information and communication technologies. This research association
from the state of Bremen links together and coordinates a multiplicity of
research teams from different universities and institutions, which are
concerned with the development and application of mobile solutions.
The series “Advanced Studies“ presents a selection of outstanding
results of MRC’s research projects.
Ulrich Türke
Efficient Methods for
WCDMA Radio Network
Planning and Optimization
TEUBNER RESEARCH
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Dissertation Universität Bremen, 2007
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Preface
WhenjoiningSiemensin2001,Ialsoextendedmyresearchinteresttowardsradionet-
workplanningmethodologies.Thisareaofresearchbroughttogethermypersonalinterest
inmobilecommunicationsandinthedesignofefficientalgorithmsanddatastructures.
Between2001and2003,IparticipatedintheEUprojectMomentum,whichwastarget-
ingtheperformanceevaluationandoptimizationofUMTSradionetworks.Inthisproject
mymainfocuswasonMonte-Carlosnapshottechniques,shortlyafterIbroadenedmy
researchscopetowardsnetworkoptimizationtechniques.Bothconstitutethebasisofthis
thesis.LaterIgotthechancetobringintheideasandmethodsofmythesisintoaSiemens
internalplanningandoptimizationtool,thedevelopmentofwhichIguidedoverthelast
twoyears.
FirstofallIwouldliketothankProfessorDr.CarmelitaGörgforsupervisingthisthesis,
forherguidance,andherencouragement.Iverymuchappreciatethatshe-althoughI,
asanexternalPh.D.student,wasnotpartofherdepartmentattheUniversityofBremen
-wasalwaysveryopenfordiscussionsandprovidedmewithalargeamountofuseful
suggestionsoverthepastyears.Iwithoutadoubtgreatlybenefitedfromthis,bothinmy
scientificworkandpersonally.Iamalsodeeplygratefulforthecommentsandsuggestions
by Professor Dr. Thomas Kürner, who is also the second examiner of this thesis. The
discussionswithhimandhisadvice-inparticularinthelastphaseoftheworkonthis
thesis-wereofgreathelptome.IamdeeplyobligedtoHelmutMühlbauerforgivingme
thechancetoconductthisthesiswithinSiemens,forhisstrongfaithinmeandmywork,
andforstrengtheningmyviewoneconomicalaspectsandthepracticalrelevanceofmy
research.FurthermoreIamverygratefultoProfessorDr.RanjitPerera,whoparticipated
asaguestscientistintheEUprojectMomentum.Iverymuchappreciatedthelongand
fruitful discussions with him - not only on technical topics. Special thanks go to my
colleaguesMichaelKoonertandRichardSchelbformanydiscussions,andnotleastfor
proof-readingpartsofthisthesis.
UlrichTürke
Abstract
Theplanningofmobileradionetworksrequiresfastandaccuratemethodsfornetwork
performance evaluation. In this thesis novel methods for the performance analysis of
WCDMAnetworksaredevelopedanddiscussed.
Thestate-of-the-artapproachforevaluatingtheperformanceofWCDMAnetworksisthe
Monte-Carlosnapshotsimulation. However,currentsolutionsbasedonsnapshotsimu-
lationshavemajorweaknesseswithrespecttoaccuracy,simulationtime,andflexibility.
Inparticularthesizeofnetworksthatcanbeanalyzedinanacceptabletimeframewith
standardhardwareresourcesistypicallyverylimited.
Thenewsnapshotanalysismethoddevelopedinthisthesissignificantlyoutperformsthe
state-of-the-artmethods.Itallowsforadetailedanalysisofverylargenetworks(Nx1000
basestationsites)onastandardPCwithinrunningtimesacceptableforeverydayplan-
ning.Thisismainlyachievedbyahighlylinearmodelingofthesystemandbyareduction
ofthesizeofthesetsoflinearequationstobecalculatedineachsnapshot.Thedimension
reductionisachievedbytheapplicationofacellbasedtransmissionpowerandinterfer-
ence level calculation as opposed to the standard mobile based approach. Despite the
significantspeed-upachievedintheevaluationofasnapshot,theproposedapproachalso
allowsaveryaccurateandflexiblemodelingofsystemfeatures,e.g.withrespecttoRadio
ResourceManagement(RRM)andQualityofService(QoS).
Increasingtheperformanceofevaluatingasinglesnapshotisonlyoneapproachtoreduce
therunningtimeofthesnapshotanalysis. Besidesthis,alsothenumberofsnapshotsre-
quiredtoyieldacertainaccuracyofresultscanbereducedbymeansofvariancereduction
techniques. Intheareaofsnapshotsimulationsfornetworkplanning,variancereduction
techniqueshavealmostnotbeenconsideredyet.Inthisthesisconsiderableefforthasbeen
putintoevaluatingtheapplicabilityofvariancereductiontechniquesforcuttingdownthe
runningtimeofthesnapshotanalysis. Basedonthediscussionofseveralvariancere-
ductiontechniquesknownfromtheory,twovariancereductiontechniquesareidentified
andtailoredtoanapplicationineverydayplanning.Both,controlvariatesandcorrelated
sampling,areappliedtoexampleplanningscenarios. Considerablegainswithrespectto
thenumberofrequiredsnapshotstoyieldacertainaccuracyofresultsareobtainedfor
theproposedvariancereductionmethods.
viii Abstract
Whileprovidingveryaccurateresultsatahighlevelofdetail,forsomeapplicationsthe
snapshot simulation approach is too time consuming. In particular search based opti-
mizationtechniques,whichcomparealargenumberofdifferentnetworkconfigurations,
requiremethodswithmuchshorterrunningtimes,whileacceptingalossinlevelofdetail
andaccuracy. Targetingsuchareastheanalyticalmethodsdevelopedinthisthesiscan
beeffectivelyapplied. Basedonastaticmethodtwonovelextendedstatisticalmethods,
incorporatingshadowfadingstatistics,areproposed. Resultsarepresentedshowingsig-
nificantimprovementsinapproximatingtheresultsfromsnapshotsimulationsintermsof
relevantsystemparameters.Finally,theapplicationofthedevelopedmethodsinnetwork
optimizationisdiscussed. Twodifferentoptimizationmethodsaredeveloped. Thepre-
sentedresultsprovethevalueofthemethodsforanapplicationinnetworkoptimization.
Themajorityofthedevelopedmethodsiscurrentlysuccessfullyappliedinaradionet-
workplanningtool.
Contents
ListofAbbreviations xiii
ListofSymbols xvii
ListofFigures xxi
ListofTables xxv
1 Introduction 1
2 TheWCDMAAirInterface 5
2.1 UMTSNetworkArchitecture . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 UTRA-FDDProtocols . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2.1 PhysicalLayer . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2.2 DataLinkLayer . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2.3 NetworkLayer . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.2.4 HighSpeedPacketAccessExtensions . . . . . . . . . . . . . . . 17
2.3 RadioResourceManagement . . . . . . . . . . . . . . . . . . . . . . . . 19
2.3.1 SoftHandover . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.3.2 PowerControl . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.3.3 AdmissionControl . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.3.4 CongestionControl . . . . . . . . . . . . . . . . . . . . . . . . . 23
3 ModelingtheWirelessTransmissionChannel 25
3.1 MacroPathLossPrediction. . . . . . . . . . . . . . . . . . . . . . . . . 26
3.1.1 BasicPropagationModels . . . . . . . . . . . . . . . . . . . . . 26
3.1.2 EmpiricalPredictionModels . . . . . . . . . . . . . . . . . . . . 28
3.1.3 ImprovedModels . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.2 ShadowFading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.2.1 ModelingofSpatialCorrelation . . . . . . . . . . . . . . . . . . 32
3.2.2 ModelingofLinkCorrelation . . . . . . . . . . . . . . . . . . . 33
3.2.3 ImplementationinSimulations . . . . . . . . . . . . . . . . . . . 34
3.3 FastFading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.4 AntennaModeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.5 SignalandInterferenceLevels . . . . . . . . . . . . . . . . . . . . . . . 39
x Contents
3.5.1 EffectiveInterferenceRaise . . . . . . . . . . . . . . . . . . . . 42
3.5.2 PowerControlHeadroom . . . . . . . . . . . . . . . . . . . . . 43
3.5.3 Soft-HandoverGain . . . . . . . . . . . . . . . . . . . . . . . . 43
4 Monte-CarloSnapshotAnalysis 45
4.1 TheMonte-CarloMethod . . . . . . . . . . . . . . . . . . . . . . . . . . 46
4.1.1 GeneralConcept . . . . . . . . . . . . . . . . . . . . . . . . . . 46
4.1.2 ConvergenceEstimation . . . . . . . . . . . . . . . . . . . . . . 47
4.2 SimpleAnalysisExample. . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.3 VarianceReductionTechniques. . . . . . . . . . . . . . . . . . . . . . . 55
4.3.1 ImportanceSampling . . . . . . . . . . . . . . . . . . . . . . . . 56
4.3.2 StratifiedSampling . . . . . . . . . . . . . . . . . . . . . . . . . 60
4.3.3 ControlVariates . . . . . . . . . . . . . . . . . . . . . . . . . . 61
4.3.4 CorrelatedSampling . . . . . . . . . . . . . . . . . . . . . . . . 63
4.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
5 SnapshotAnalysisforWCDMANetworkPerformanceEvaluation 67
5.1 BasicAnalysisLoop . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
5.2 SnapshotGeneration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
5.3 ModelingofDynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
5.4 SystemConstraintsandLimits . . . . . . . . . . . . . . . . . . . . . . . 71
5.5 CellBasedRadioPerformanceEvaluation . . . . . . . . . . . . . . . . . 73
5.5.1 UplinkEquations . . . . . . . . . . . . . . . . . . . . . . . . . . 75
5.5.2 DownlinkEquations . . . . . . . . . . . . . . . . . . . . . . . . 77
5.5.3 SoftHandover . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.5.4 IterativeSolution . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.6 ResourceScheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
5.6.1 QoSConcept . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
5.6.2 AnalysisAlgorithm. . . . . . . . . . . . . . . . . . . . . . . . . 83
5.6.3 ApplicationExample . . . . . . . . . . . . . . . . . . . . . . . 89
5.7 MethodExtensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
5.7.1 MultiCarrierAnalysis . . . . . . . . . . . . . . . . . . . . . . . 92
5.7.2 HSPAAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
5.8 ApplicationofVarianceReductionTechniques . . . . . . . . . . . . . . 93
5.8.1 ControlVariates . . . . . . . . . . . . . . . . . . . . . . . . . . 94
5.8.2 CorrelatedSampling . . . . . . . . . . . . . . . . . . . . . . . . 102
5.9 ValidationofResults . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
5.9.1 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
5.10 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
6 AnalyticalPerformanceEvaluation 107
6.1 StaticLoadEstimation . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
6.1.1 AverageRequiredLinkPower . . . . . . . . . . . . . . . . . . . 108
Contents xi
6.1.2 CellAssignment . . . . . . . . . . . . . . . . . . . . . . . . . . 111
6.1.3 ServingProbability . . . . . . . . . . . . . . . . . . . . . . . . . 111
6.1.4 CoverageProbability . . . . . . . . . . . . . . . . . . . . . . . . 111
6.1.5 CharacteristicCellPowerEquations . . . . . . . . . . . . . . . . 111
6.1.6 ApplicationExample:StaticLoadEstimation . . . . . . . . . . . 113
6.2 StatisticalLoadEstimation . . . . . . . . . . . . . . . . . . . . . . . . . 116
6.2.1 ServingProbabilities . . . . . . . . . . . . . . . . . . . . . . . . 116
6.2.2 CoverageProbability . . . . . . . . . . . . . . . . . . . . . . . . 117
6.2.3 CharacteristicCellPowerEquations . . . . . . . . . . . . . . . . 118
6.2.4 ApplicationExample:StatisticalLoadEstimation . . . . . . . . . 119
6.3 ExtendedStatisticalLoadEstimation. . . . . . . . . . . . . . . . . . . . 120
6.3.1 AverageRequiredLinkPower . . . . . . . . . . . . . . . . . . . 120
6.3.2 CharacteristicCellPowerEquations . . . . . . . . . . . . . . . . 125
6.3.3 Application:ExtendedStatisticalLoadEstimation . . . . . . . . 126
6.4 EvaluationofPerPixelQuantities . . . . . . . . . . . . . . . . . . . . . 127
6.4.1 PilotRSCP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
6.4.2 Soft-HandoverProbability . . . . . . . . . . . . . . . . . . . . . 130
6.4.3 PilotQuality . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
6.4.4 DownlinkConnectionPower . . . . . . . . . . . . . . . . . . . . 133
6.4.5 UplinkConnectionPower . . . . . . . . . . . . . . . . . . . . . 135
6.5 PracticalImplementation . . . . . . . . . . . . . . . . . . . . . . . . . . 135
6.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
7 AutomatedNetworkOptimization 137
7.1 OptimizationParametersandTargets . . . . . . . . . . . . . . . . . . . . 137
7.2 OptimizationbasedonFastHeuristic . . . . . . . . . . . . . . . . . . . 138
7.3 SearchBasedOptimization . . . . . . . . . . . . . . . . . . . . . . . . . 142
7.3.1 ABriefIntroductiontoLocalSearch. . . . . . . . . . . . . . . . 142
7.3.2 ApplicationofLocalSearchtoRadioNetworkPlanning . . . . . 145
7.3.3 AdvancedSearchAlgorithm . . . . . . . . . . . . . . . . . . . . 147
7.4 ApplicationExample . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
7.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
8 ConclusionsandOutlook 155
A GeneratingCorrelatedRandomVariables 157
Bibliography 159