GTI 5G-A 无线网络智能评估系统白皮书

GTlGTI5G-AWirelessNetworkIntelligenceEvaluationSystemWhitePaper

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GTI5G-AWirelessNetwork

IntelligenceEvaluationSystemWhite

Paper

Version:

v_1.0.0

DeliverableType

□ProceduralDocument

⑦WorkingDocument

ConfidentialLevel

⑦OpentoGTIOperatorMembers

⑦OpentoGTIPartners

□OpentoPublic

Program

5GTechnologyandProduct

Project

TechnologyEvolution

Task

5G-ATechnology

Sourcemembers

ChinaMobile,Huawei,ZTEandVIAVISolutions

Supportmembers

Ericsson

Editor

JiachenZhang(CMCC),LeiCao(CMCC),TianmingJiang(CMCC),JingGao(CMCC),ZheLi(Huawei),XiongqinshuiGan(Huawei),PanLi(ZTE),DonghongOuyang(ZTE),JianguoLi(ZTE),Jun

Yang(Viavi),YaningMa(Viavi)

LastEditDate

2024-11-18

ApprovalDate

Confidentiality:ThisdocumentmaycontaininformationthatisconfidentialandaccesstothisdocumentisrestrictedtothepersonslistedintheConfidentialLevel.Thisdocumentmaynotbeused,disclosedorreproduced,inwholeorinpart,withoutthepriorwrittenauthorizationofGTI,

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TableofContents

GTI5G-AWirelessNetworkIntelligenceEvaluationSystemWhitePaper 2

DocumentHistory 4

TableofContents 5

ExecutiveSummary 7

Abbreviations 8

Introduction 9

1WirelessNetworkIntelligenceEvaluationSystem 10

1.1EvaluationSystem 10

1.1.1ApplicationScenarios 11

1.1.2EvaluationEnvironments 11

1.1.3EvaluationTools 11

1.1.4EvaluationIndicators 11

1.1.5TestInterfaces 12

1.1.6EvaluationSpecifications 12

1.2ApplicationScope 12

2TypicalScenarios 14

2.1SmartServiceProfiling 14

2.1.1ApplicationScenarios 14

2.1.2EvaluationEnvironments 15

2.1.3EvaluationTools 16

2.1.4EvaluationIndicators 16

2.1.5EvaluationSpecifications 17

2.2IntelligentAMC 17

2.2.1ApplicationScenarios 17

2.2.2EvaluationEnvironments 19

2.2.3EvaluationTools 19

2.2.4EvaluationIndicators 19

2.2.5EvaluationSpecifications 20

2.3IntelligentMU-MIMO 20

2.3.1ApplicationScenarios 20

2.3.2EvaluationEnvironments 21

2.3.3EvaluationTools 21

2.3.4EvaluationIndicators 22

2.3.5EvaluationSpecifications 23

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2.4IntelligentMIMOSleep 23

2.4.1ApplicationScenarios 23

2.4.2EvaluationEnvironments 24

2.4.3EvaluationTools 25

2.4.4EvaluationIndicators 25

2.4.5EvaluationSpecifications 26

3DevelopmentTrends 27

4SummaryandProspects 30

5References 31

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ExecutiveSummary

Asnext-generationinformationtechnologieslikeAIandbigdatacontinuetodrivedigitalandintelligenttransformationinindustries,ChinaMobileiscapitalizingonthedigitaleconomybydevelopingacutting-edge"connection+computing+capability"informationservicesystem.Thesystemleverages5G,computingnetworks,andAbilityasaService(AaaS),expectedtopromotetheconstructionofintelligentnetworks.

Intelligentwirelessnetworksarecrucialfornetworkintelligence.Theircapabilityevolutionnotonlydeterminestheiterativeupgradesofthecommunicationsnetworkarchitecturebutalsoaffectstheassuranceandimprovementofservicessuchasnetworkenergysavingandairinterfacetransmission.Assuch,AImustbeintroducedandintegratedintomobilecommunicationsnetworks,encompassingtheirarchitectures,functions,andkeytechnologies,toacceleratethenetworkevolutiontowardsintelligencethroughsmartprofiling,prediction,optimization,anddecision-making.Thiswillfacilitatethedigitalandintelligenttransformationofbotheconomyandsociety.

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Abbreviations

Abbreviation

Explanation

AaaS

AbilityasaService

AI

ArtificialIntelligence

AMC

AdaptiveModulationandCoding

BLER

BlockErrorRatio

CCSA

ChinaCommunicationsStandardsAssociation

CQI

ChannelQualityIndicator

DOA

DirectionOfArrival

FTP

FileTransferProtocol

KPI

KeyPerformanceIndicator

MCS

ModulationandCodingScheme

MTTR

meantimetorestoration

MU-MIMO

Multi-UserMultiple-InputMultiple-Output

NE

networkelement

OMC

operationandmaintenancecenter

OSS

operationssupportsystem

PCI

physicalcellidentifier

PDCP

PacketDataConvergenceProtocol

PDSCH

PhysicalDownlinkSharedChannel

PMCounter

PerformanceManagementcounter

PRB

PhysicalResourceBlock

RF

radiofrequency

RI

RankIndicator

RRC

RadioResourceControl

SE

SpectralEfficiency

UE

UserEquipment

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Introduction

Next-generationinformationtechnologieslikeAIandbigdataarenowdrivingdigitalandintelligenttransformationacrossvariousindustriesandarekeytoimprovingnetworkefficiencyandcapabilities.ForChinaMobile,theconstructionofintelligent5G-Awirelessnetworksisessential.ItservesasthefoundationforChinaMobile'svisionofnetworkintelligenceanddrivecontinuousadvancementsinnetworkcapabilities.

However,theindustryisstillintheapplicationexplorationandcapabilitybuildingphaseintermsof5G-Awirelessnetworkintelligence.TheoverallR&Dinvestmentinintelligentproductslagsbehindexpectations.Againstthisbackdrop,ChinaMobileproposesacomprehensivesystemforevaluatinghowintelligenta5G-Awirelessnetworkis.Bypromotingevaluation-drivenexplorationandinvestment,weaimtoprovideindustrystakeholderswithareferenceandguidanceforplanning,designing,evaluating,andverifyingtechnologies,solutions,andproductsrelatedto5G-Awirelessnetworkintelligence.

Thiswhitepaperillustratestheevaluationsystemandshowcasespracticalapplicationscenarios.ItispreparedbyChinaMobileandco-authoredbyHuawei,ZTEandVIAVISolutions.

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1WirelessNetworkIntelligenceEvaluationSystem

TheconvergenceofAItechnologieswithhardware,software,systems,andprocessesincommunicationsnetworksgivesrisetonetworkintelligence.AItechnologiesmaketheoperationofcommunicationsnetworksintelligent,enableagileserviceinnovations,andusherinnetworkswithnativeAI.Thisresultsinhigherqualityandefficiencyofcommunicationsnetworksandsupportsthedigitalintelligenceofindustries.Asasubfieldandinfrastructureofnetworkintelligence,wirelessnetworkintelligenceintegratesAIalgorithmsandcomputingpowerwithexisting5Gand5G-Anetworkelements(NEs),unlockingthefullpotentialofAIonnetworksanddrivingimprovementsinperformanceandefficiency.

1.1EvaluationSystem

WiththeevolutionofwirelessAIapplications,traditionalindicator-basedevaluationsystemsfallshortinmeetingthediverserequirementsof5G-Aintelligenceevaluationscenarios.Moreover,designingevaluationindicatorsforasinglepurposeproveschallenging.Therefore,networkintelligenceevaluationsystemsrequireincrementalimprovementonascenario-by-scenariobasis.Tomeetthisrequirement,weproposethe"1-4-1"evaluationarchitecture,asshowninthefollowingfigure.

Thearchitectureisdesignedbasedontypicalscenariosandincorporatesfourkeyelements:evaluationenvironments,tools,indicators,andinterfaces.Westandardizecommonissuesineachelement,inabidtofosterindustry-wideconsensusonwirelessnetworkintelligence.Thisarchitecturealsocoversbothtop-downdesignandmethodology,guidingtheindustrytograduallyimprovewirelessnetworkintelligenceevaluationsystems.

Figure1"1-4-1"evaluationarchitecture

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Intheprecedingfigure,thebluesectionsrepresentcontentinvolvedinthetestcasesofthiswhitepaper,whilegreenareasdenoterecommendedconsiderationsforfutureevaluationwork,thoughnotexplicitlydetailedhere.

1.1.1ApplicationScenarios

Aswirelessnetworksgrowincreasinglycomplex,AIplaysavitalroleinoptimizingtheirperformance.BytailoringAIcapabilitiestoaddressspecificlive-networkissues,networkcapabilitiescanbeenhancedandoperationalefficiencyimproved.Therefore,scenario-specificcasestudiesareessentialtoeffectivelyevaluatewirelessnetworkintelligence.Thiswhitepapercoversseveraltypicalscenarios.

1.1.2EvaluationEnvironments

Commercialwirelessnetworkscanbeclassifiedintodistinctcategoriesbasedonfactorslikeindoor/outdoorcoverage,speeds,levelsofinterference,andcapacityrequirements.Incomplexenvironments,AIwithitsexceptionalfittingandgeneralizationabilitieswilllikelyplayapivotalrole.Whendesigninganevaluationenvironment,itisessentialtoconsidertheanticipatedAIcapabilitiesonthenetwork.Forinstance,AI-poweredpredictioncanbeusedinhigh-speedrailwayscenariostooptimizeresourceallocationinhigh-speedcellsthatUEsmayaccess.IfsuchanativeAIsolutionistobeevaluated,itmustbetestedinhigh-speedrailwayscenariostoensureitsefficacy.Similarly,evaluatingintelligentNEsnecessitatesconsideringtheirintendedapplicationstounlocktheirfullpotentialinthechosenenvironment.

1.1.3EvaluationTools

Toeffectivelyevaluatewirelessnetworkintelligence,itisessentialtodefinethenecessarytoolsandinstruments,includingtheirkeyfunctionsandrequirements.Giventheblack-boxnatureofAI,evaluationinstrumentsmustofferatleastthefollowingfunctions:simulatingcorenetworkandUEfunctions,replayingandtracingservices,monitoringdata,andgeneratingwirelessandservicedata.Whilecommontoolslikedatamonitoringsoftwarecanbewidelyapplied,differentiatedtoolfunctionsarealsonecessary.Forinstance,toevaluatenetworkperformanceinserviceassurance,toolsneedtoprovideservicerecordingandreplaycapabilities,whereasend-to-endcoordinationtestscenariosdemandsimulationofcorenetworkandUEfunctions.Specificcasesaredetailedinsubsequentsectionsforfurtherclarification.

1.1.4EvaluationIndicators

Toquantifytheeffectsofintelligence,bothkeyindicatorsandobservationindicatorsmustbespecified.Keyindicatorsserveasthefoundationforobservationindicators.SincetraditionalevaluationmethodsrelyingonsimpleindicatorscannotcopewithvariousAI-drivenscenarios,observableanddiversifiedevaluationindicatorsmustbeformulatedbasedonspecificscenariocharacteristicstoevaluatehowintelligentwirelessnetworksare.Commonwirelessnetworkindicatorscanbeusedforevaluation,buttheirapplicationmustmatchscenarios.Forinstance,shutdowndurationservesasakeyindicator,whilebasestationenergyconsumptionisanobservationone,whenassessingintelligentchannelshutdownperformance.Similarly,

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thenumberofRRCconnectionsfunctionsasakeyindicator,whereastheuser-perceivedrateactsasanobservationindicator,whenevaluatingtheeffectivenessofintelligentMU-MIMO.

Thiswhitepaperexplainstheseevaluationindicatorsanddemonstratestheirpracticalitythroughconcreteexamples.Theevaluationmethodspresentedinthefollowingsectionsareintendedsolelyforverificationpurposes,anddonotrepresentindustrystandardsorrequirements.

1.1.5TestInterfaces

Unliketraditionalcommunicationsfunctions,AIapplicationsarefunctioningwhilekeepingkeyalgorithmsconcealed.TogaugetheeffectofaspecificAIapplication,indicatorsmustbepresentedusingstandardizedinterfaces.Theseinterfacescanworkwithevaluationtoolstoprovidereal-timedisplayoftheintelligenceleveloftheembeddedAIcapability.

Testinterfacessupportbothofflineoutputandreal-timeoutput.ForAIapplicationsintrainingmode,datatobecollectedincludesofflinedatasuchasmodelinitializationparametersandtrainingdatasetsaswellasreal-timedatathatenablesNEstotrainandupdatemodelsonlinewhennetworkcapabilitiespermit.ForAIapplicationsininferencemode,datatobecollectedincludestherunningstatus,computingpowerusage,runningefficiency,andstoragespace.Whentestinterfacesareusedinfuturewirelessnetworkapplications,AImodelscanbeupdatedpromptlyandtheirperformancecanbemonitoredinrealtime.

Duetoitsnascentstage,therelevantresearchisnotdiscussedinsubsequentsections;nonetheless,itisworthnotingthattestinterfacesholdsignificantswayoverintelligencecapabilityopennessandstandardization.

1.1.6EvaluationSpecifications

Toguaranteethescientificandstandardizedexecutionofevaluationactivities,evaluationspecificationsmustberobustenoughtovalidatetherelevanceofevaluationenvironments,tools,indicators,andAIinterfacesagainstindustrystandardsandspecifications.Contentthatcanbestandardizedneedstobeidentifiedaswelltoenhancethestandardizationofbothinternationalandnationalevaluationsystems.Forinstance,interfacedefinitions,datatypes,input,andoutputofnetworkcapabilitiesshouldbedevelopedbasedontherequirementsofcorrespondingtechnicalsystems,whichmayincludedata,interfaces,andprocessestofacilitatethestandardizedimplementationofevaluationactivities.

1.2ApplicationScope

Thenativeintelligenceforwirelessnetworks,enabledbyAI,reducescostsandimprovesefficiencyintermsofradiosignalprocessingandradioresourcemanagementandalsoempowersserviceprofilingandevaluation.Thisintelligencealsoinvolveselementsforintelligentizationsuchasdatasetsprovidedbybasestationsandwirelessoperationandmaintenancecenters(OMCs)tooperationssupportsystems(OSSs)andupper-layerAIapplications.

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Theperformanceevaluationofnativeintelligenceforwirelessnetworkshingesonthe

deploymententitiesofAImodelinferenceservices.Specifically,whenAImodelinferenceisdeployedonbasestationsandwirelessOMCs,theymustbeincludedastheevaluatedobjects.Whenothercommonordedicateddevicesandcomponentsserveastrainingentities,theyshouldbeincludedintheevaluationaswell.

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2TypicalScenarios

Thenetworkcanapplyintelligenceinvariousways.Forinstance,AI-poweredanalysishelpsprofileandevaluatenetworkservices,whileAI-optimizedplanningenablesthenetworktoadjustitssettingsaccordingtospecificscenarios.Thesediverseapplicationsnecessitateanevaluationapproachforintelligentnetworksthatmovesbeyondrelianceonafixedsetofindicators.Forexample,recallandprecisionratescanbeusedtoevaluateanAIclassificationalgorithm,whereasaconvexoptimizationalgorithmmustbeevaluatedintermsofitsconvergencerateandsolutionstability.Suchanevaluationapproachmustalsodefinethescenariosinwhichanalgorithmtakeseffectonthenetworkandwhattoolsareemployedtomonitorthealgorithm'sperformance.Thischapteraimstodescribetailoredsolutionsforevaluatinghowintelligentawirelessnetworkisinspecificscenarios,coveringbothtop-downdesignandmethodology.Thedescriptionsinthischapterareorganizedinlinewiththe"1-4-1"evaluationarchitecture.

2.1SmartServiceProfiling

2.1.1ApplicationScenarios

ServiceprofilingontheRANsideisacriticaltechnologythatgivesasignificantcompetitiveedgetodifferentiatedserviceassurance.Withoutthistechnology,thenetworkcannottaketargetedmeasurestoensureservicequality.Thistechnologyalsoplaysavitalroleindevelopingnewservices,improvinguserexperience,andreducingusercomplaints.

Figure2Smartserviceprofiling

Serviceprofilingintegratescloud-basedtrainingwithlocalinferencetocreateaccurateprofilesofservices.ItleveragesadatabaseofservicefeaturescreatedbyanAImodelthatextractskeyuserfieldsandpacketfeatures.Specifically,theAImodelfirstcollectstrafficfeaturesofpackets(suchascertainfieldvalues,packetsize,andinter-packetintervals).Usingsemi-supervisedlearning,themodelthenlearnsthecorrelationbetweenthesetrafficfeaturesandcorrespondingservicetypesfromlabeledservicesamplesprovidedbyservicedialingtests.

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Figure3Processofserviceprofiling

2.1.2EvaluationEnvironments

Evaluationofserviceprofilinginvolves(1)capturingrealpacketsforservicesonvariousapps;(2)usinganinterfaceprotocoltestertoreplaythelive-networkservicesforthenetworktoperformserviceprofiling.

Figure4Processofserviceprofilingevaluation

Packetcaptureenvironment:UseatooltocapturesufficientpacketsforUEs'realservicesonthelivenetwork(suchasshortvideowatching,uplinklivestreaming,andonlinegaming).Thecapturedpacketswillbeusedforservicereplay.

Mainlycapturepacketsforencryptedservicesonthelivenetwork,becauseAIhasastrongcapabilitytoanalyzeencryptedservices.Ontheotherhand,toensuretheintegrityoftheevaluation,captureafewpacketsforunencryptedservicesaswell.

Figure5Networkingforcapturingpackets

Servicereplayenvironment:Useaninterfaceprotocoltestertoreplaytherealservicesonthebasestation.Thebasestationwillreceivedatafromthetesteranddeterminethetypeofthereplayedservice.

Figure6Networkingforreplayingservices

Duringthereplayofrealservices,thecapturedpacketsforrealservicesaresentfromtheinterfaceprotocoltestertothecorenetwork,basestation,andUEinsequence;ACKsfromthe

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UEaresenttothebasestation,corenetwork,andinterfaceprotocoltesterinsequence.Inthisprocess,thebasestationprofilestheservicesreplayedbytheinterfaceprotocoltester.Theprofilingresultsofthebasestationarethencomparedwiththeservicesreplayedbythetestertocheckwhethertheyareconsistent.Thisishowtheaccuracyofserviceprofilingisevaluated.

2.1.3EvaluationTools

Evaluationofserviceprofilingrequiresthefollowingtools:

Packetcapturetool:ItisanappinstalledonUEs.Onceinstalledandopened,thisappwillcapturepacketsforallsubsequentservices.Whenusingthisapp,mainlycapturepacketsforencryptedservicesonthelivenetworktoevaluateAI'scapabilitytoprofileencryptedservices;captureafewpacketsforunencryptedservicesaswelltoensuretheintegrityoftheevaluation.

Servicereplaytool:Itisusedtoreplayrealservices(thatis,toreplaycapturedpacketsforserviceslikeshortvideos,longvideos,andmobilegamesonUEs).ThedataflowsforthereplayedservicesgotothebasestationinthedownlinkandtoUEsintheuplink.

Figure7Networkingforanddataflowsfromaninterfaceprotocoltester

Serviceanalysistool:ItisusedtorecordUElogs,analyzeUEandservicestatus,andcheckUEs'networkconnection,radioenvironment,physicalcellidentifiers(PCIs)ofservedcells,andotherinformation.

Figure8Serviceanalysistool

2.1.4EvaluationIndicators

Serviceprofilingisevaluatedbycomparingtwokeyindicators:thenumberoftimesthataUEperformsaserviceandthenumberoftimesthatthebasestationcorrectlyprofilestheservice.

Numberoftimesthatthebasestationcorrectlyprofilesaservice:ThisindicatormeasureshowmanytimesthebasestationcorrectlyprofilesaserviceperformedbyaspecificUEwithina

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specifiedperiod(forexample,10minutes).WhetheraprofilingresultiscorrectdependsontheactualserviceontheUE.

NumberoftimesthataUEperformsaservice:ThisindicatorisincrementedbyoneeachtimeaUEperformsaservicesuchasshortvideowatchingwithinaspecifiedperiod(forexample,10minutes).

Evaluationofserviceprofilingusesthe"serviceprofilingaccuracy"astheindicatorforobservation.Thisindicatoriscalculatedasfollows:

Serviceprofilingaccuracy

∑Numberoftimesthatthebasestationcorrectlyprofilesaservice

∑NumberoftimesthataUEperformstheservice

=x100%

Inmostcases,theaverageserviceprofilingaccuracy(includingbothencryptedandunencryptedservices)shouldbe95%orhighertoensuresoundserviceassuranceanddeterministicexperience.

2.1.5EvaluationSpecifications

Therearecurrentlynoindustry-widespecificationsforevaluatingsmartserviceprofiling.Nevertheless,ChinaCommunicationsStandardsAssociation(CCSA)ismovingtodevelopthetechnicalrequirementsfor5Gmobileserviceexperiencequality[1].Thisexemplifiesthatuserexperienceandsustainablenetworkdevelopmentaretakingcenterstageinbuilding,optimizing,andoperating5Gmobilenetworks.Inthefuture,itiscrucialtocontinueresearchingtheevaluationofsmartserviceprofilingandestablishwidelyacceptedspecificationsacrossthetelecommunicationsindustry.

2.2IntelligentAMC

2.2.1ApplicationScenarios

Thewirelesschannelisinfluencedbycomplexphysicalenvironmentalfactors,suchaspathloss,multi-pathfading,anddopplerfrequencyoffset,resultingintime-frequencydualfast-varyingcharacteristics.Thiscausesrapidfluctuationsinthechannelqualitybetweenabasestationandaterminalwithinmillisecond-leveltimeslots.Furthermore,complexandirregularinterferencesignalsexacerbateperformancevariabilityacrosstimeslots,whereuniformschedulingstrategiesmaywastespectralresourcesinhigh-qualityslotsandleadtosignificantpacketlossinlow-qualityslots,causingsharpperformancedegradation.

Incurrentnetworks,notableinter-slotperformancedisparitiesoccurinscenariossuchasatmosphericductingcausingunevenslotinterference,differentpilotstructuresbetweenS-slotsandD-slotsinhigh-speedrailsettings,andinterferenceinspecificoverlappingslotsundermacro-micronetworking.Forexample,inatmosphericductingscenarios,demodulationdifferencesbetweentimeslotsundertraditionalschedulingstrategiesresultinlow-performingslotsdraggingdownhigh-performingones,therebyimpactingsystemperformanceanduserexperience.Therefore,thekeytoaddressingrateinstabilityinsuchscenariosistheintelligentandadaptiveadjustmentofschedulingstrategiestoswiftlyandefficientlymatchchannelvariations.

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Figure9Atmosphericductinterferencediagram

ThemainconceptofIntelligentAMC(AdaptiveModulationandCoding)isusingclusteringalgorithmstogroupdifferenttimeslotsforthesameterminalbasedonchannelcharacteristics,applyinghomogeneousschedulingstrategieswithinclustersandheterogeneousstrategiesbetweenclusters.Thisenablesdifferentiatedschedulingaccordingtohigh-orderchannelfeaturesofeachslot,therebyfullyexploitingtheperformancepotentialofbothhigh-andlow-qualityslots.Tofurtherreducetime-frequencyoverheadfromexploratoryprocessesandacceleratetheconvergenceofintra-clusterschedulingstrategies,bigdataisusedtotrainaschedulingoptimizationnetwork,integratinghistoricalfeaturedatatopromptlyadjustexistingstrategiesandquicklyconvergetotheoptimalschedulingposition.Combiningthesetwoapproachesallowsforstableschedulingacrosstimeslotsinrapidlyvaryingchannel/interferencescenarios,reducingtheimpactofanomalousslotsonschedulingstabilityandmaximizingspectralefficiency.

Thedesignoftheclusteringalgorithmiscentraltothisintelligentnetworkelement.Theclusteringalgorithmcategorizessamplepointsbasedonthesimilarityofselectedwirelessfeatures,ensuringhighintra-clustercohesionandlowinter-clustercoupling.Thisapproacheffectivelydifferentiateshigh-qualityfromlow-qualitychannels,mitigatingtheimpactofenvironmentalvariationsonschedulingstrategies,maintainingstabilityinthesystem’sBlockErrorRate(BLER),andmaximizingthecapacitypotentialofeachtimeslot.

Figure10Userclusteringflowchart

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2.2.2EvaluationEnvironments

Thisapplicationfocusesontheuseofclusteringalgorithmstoapplydifferentiatedschedulingstrategiestohigh-andlow-qualitytimeslots,thusleveragingtheperformancegainsfromselectivelyreleasingcapabilitiesacrosstimeslots.Thiscapabilityshowssignificantbenefitsinscenarioswithslotimbalance.Accordingly,duringtesting,thisexampleconstructsscenarioswheretimeslotperformancediffersmarkedly,usingBLERandspectralefficiencymetricstodemonstratethereliabilityandeffectivenessofthealgorithm.

Inlaboratorytesting,thisexampleconstructsasingle-user,indoorwiredenvironmentwithcontrolledinterference,whereinterferenceisartificiallyintroducedintofixedtimeslotstogenerateperformancevariationsacrosstimeslots.Forcommercialsitetesting,sitesinacommercialnetworkareselectedwherethereareatleast105Gusersandaminimumof2typesofcommercialterminals.Pointswithsubstantialslotschedulingvariationareselected,specificallythosewheretheBLERdifferencebetweenhigh-andlow-qualityslotsexceeds20%.Atthesepoints,aUEisconnectedtoperformsingle-userFTPtraffic.

2.2.3EvaluationTools

Inlaboratorytesting,asingleUEperformsfull-loadFTPtraffic,usingavectorsignalgeneratorasatestingtool.Interferenceisintroducedatfixedtimeslots,withtheformofinterferenceunrestricted,aslongasittransmitsinafixeddownlinkslot.Theinterferenceintensityi