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July2022
TITLE
doc.:IEEE802.11-22/0987r
21
Submission page
PAGE
3
XiaofeiWang(InterDigitalInc.)
IEEEP802.11
WirelessLANs
IEEE802.11AIMLTIGTechnicalReportDraft
Date:2022-07-06
Author(s):
Name
Affiliation
Address
Phone
XiaofeiWang
InterDigitalInc.
111West33rdStreet
NewYork,NY10120
USA
+1-607-592-2727
Xiaofei.wang@
MingGan
Huawei
Ming.gan@
ZinanLin
InterDigital
RuiYang
InterDigital
AiguoYan
Zeku
JunghoonSuh
Huawei
ZiyangGuo
Huawei
MarcoHernadez
NICT
LiangxiaoXin
Zeku
Abstract
ThisdocumentcontainsthetechnicalreportoftheIEEE802.11AIMLTIG.
R0:initialoutline
R1:insertionofUsecase1
R2:insertionofIntroduction
TableofContents
Introduction
Terminologies
AIML ArtificialIntelligence/MachineLearning
CSI ChannelStateInformation
UHR UltraHighReliability
Backgroundinformation
ArtificialIntelligence/MachineLearning(AI/ML)algorithmshavemadesignificantprogressandarebeingappliedinmanydomains,includingmedicaldiagnosis,speechrecognition,computervision,andintegrationofvisionandcontrolforrobotics.Inaddition,AI/MLalgorithmsareemergingasimportantcomponentsinmanyapplicationssuchasautonomousdriving,languagetranslationandhuman-machineinteractions.
TraditionalAI/MLtechniquesarebasedonacentralizedmodelwhichrequiresexchangingalargeamountofdatabetweendatasourcesandacentralizedserver.Morerecently,distributedAI/MLalgorithmssuchasfederatedlearninghavebeendevelopedthatwillallowmoreanalysisatthesourceandreducetheamountofdatathatneedtobeexchanged,thoughtheexpectedamountofexchangeddataremainssignificant.Withtheprevalenceofwirelessnetworksandcommunications,muchoftheexchangeddataisexpectedtobecarriedthroughwirelessnetworks,suchasIEEE802.11WLANnetworks.
StudieshaveshownthatAI/MLalgorithmscanhelpimprovetheperformanceforwirelesscommunicationnetworks,byprovidingbetterresourceusage,lowerenergyconsumption,higherreliabilityandmorerobustnesstoachangingenvironment.Asthesealgorithmsbecomemorematureandcosteffective,WLANmayleverageAI/MLforenhancednetworkperformanceanduserexperience.
InMay2022,theIEEE802.11WorkingGroup(WG)hasapprovedtheformingoftheAIMLTaskInterestGroup(TIG)bythefollowingmotion[1]:
Motion5:TIGRe:AI/MLusein802.11
ApproveformationofaTopicInterestGroup(TIG)to:
(a)describeusecasesforArtificialIntelligence/MachineLearning(AI/ML)applicabilityin802.11systemsand
(b)investigatethetechnicalfeasibilityoffeaturesenablingsupportofAI/ML.
TheTIGistocompleteareportonthistopicatorbeforetheMarch2023session.
ThistechnicalreportisthefinalreportoftheAIMLTIGtotheIEEE802.11WGdetailingvariousAIMLusecasesdiscussedduringtheAIMLTIG.Foreachusecase,anumberofKeyPerformanceIndicators(KPIs)havebeenidentifiedandrequirementsandtechnicalfeasibilityanalysishavebeenprovided.
AIMLUsecasesforIEEE802.11
Note:usecasespotentiallycanbeorganizedintodifferentcategories
Note:usecasespotentiallycanidentifyKPIs
Usecase1:CSIfeedbackcompression
In802.11ax[1]andthedraftof802.11be[2],theAPinitiatesthesoundingsequencebytransmittingtheNDPAframefollowedbyaNDPwhichisusedforthegenerationofVmatrixatthebeamformee.UponthereceiptoftheNDPfromthebeamformer,thebeamforeeappliesacompressionscheme(i.e.,Givensrotations)ontheVmatrixandfeedsbacktheangelesinthebeamformingreportframe.
Itisindicatedin
REF_Ref118889474\r\h
[4][3]
thathighernumberofspatialstreamshasbeenaninevitabletrendinWiFiformorethanadecade.Theprelimilaryresults
REF_Ref118889474\r\h
[4][3]
REF_Ref118889476\r\h
[5][4]
REF_Ref118889495\r\h
[6][5]
showthatMIMOwithalargenumbertransmitterantennasandalargenumberofspatialstreams(e.g.,16spatialstreams)offerremarkablesystemperformancegainsonbothSU-MIMOandMU-MIMOcases.MultiAP(MAP)maybeonepotentialfeatureinthenext802.11generation,e.g.UHR
REF_Ref118797206\r\h
[7][6]
-
REF_Ref118796138\r\h
[10][9]
.LargenumberofspatialstreamscombinedwithMAPfeaturemayfurtherincreasethesoundingfeedbackairtimeoverheadifcoordinationbetweenAPs(e.g.,jointtransmission/reception,coordinatedbeamforming)isapplied.Largeamountofoverheadorprolongedsoundingproceduresmaynegativelyimpactthelatencyandlimitthesystemperformance.Therefore,thereisaneedtoreducetheCSIoverheadespeciallywhenthenumberoftransmitterantennasgoeshigherormultipleAPsperformjointorcoordinatedtransmission.
Somestudies(e.g.,
REF_Ref118797710\r\h
[11][10]
REF_Ref118797712\r\h
[12][11]
REF_Ref118983623\r\h
[13][12]
REF_Ref118988666\r\h
[14][13]
)haveshownthatAI/MLcanefficientlyreducetheCSIfeedbackandimprovethesystemthroughput.Forexample,motivatedbythenaturethattheCSImayfallintodifferentclustersduetothechannelsimilarityofnearbySTAs,iFORalgorithm
REF_Ref118797710\r\h
[11][10]
appliestheunsupervisedlearning,K-mean,totheCSIcompressiontoclassifytheanglevectorswhicharederivedfromVmatrix.Simulationresultsshowthatfora8x2SU-MIMO,iFORusesaround8%ofthenumberofbitsrequiredbytheexistingfeedbackmechanism(802.11ax)andboostthesystemthroughputbyupto52%.In
REF_Ref118797712\r\h
[12][11]
,anotherunsupervisedlearning,DeepNeuralNetworkAutoencoder(DNN-AE)isappliedtoCSIanglevectorsandfurthercompressesthederivedangles(LB-SciFi)byleveraingthecompressioncapabilityofDNNs.ExperimentalresultsshowthatLB-SciFireducesthefeedbackoverheadby73%andincreasesthenetworkthroughputby69%onaverage.
ThisusecaseproposestoapplyAI/MLtechniquetoCSIfeedbackschemestoreducetheCSIoverheadwithminimumlossofPERperformance.
KPIsconsideredinthisusecaseareproposedasfollows:
Numberoffeedbackbitspersubcarriergroup
AchievedPER
BothSU-MIMOandMU-MIMOcasesneedtobeconsidered
AdditionalAIMLoverheadcompredwithcompressionsaving
OneexampleistheratiobetweenthenumberofadditionalbitsrequiredbyAIMLprocess(includingdatausedformodeltraining/inference
REF_Ref119303357\r\h
[15][14]
themodelparameters,theadditionalsignaling)andthenumberofbitssavedbytheCSIfeedbackscheme.Inthisexample,ifthedatausedformodeltrainingthatisperformedbytheAPfullyreliesonthelegacyCSIreport,thentheadditionalAIMLusedformodeltraining/inferencemaybe0.
Computationcomplexity/Latency:
AdditionaldelayorcomputationisintroducedbyAIMLprocessing
Eveluationmethodologyneedstobeestablished.
Usecase2
UsecaseN
RequirementsandPotentialfeaturesanalysis(highlevel)
Requirements
RequirementsUsecase1:CSIfeedbackcompression
Performanceshouldfollowtheguidiancebelow:
CSIairtimereduction:achievearitimereductionofCSIfeedbackover802.11beforagivenNrxNcMIMO,whereNristhenumberofrowsinthecompressedbeamformingfeeedbackmatrix,Ncisthenumberofcolumnsinthecompressedbeamformingfeedbackmatrix.
AdditionaloverheadusedforAIMLprocess:minimizetheadditionaloverheadusedforAIMLprocess.AdditionaloverheadmayincludethedatausedforAIMLmodeltraining/inference[14],themodelparametersandadditionalsignalling.ThedatausedforAIMLmodeltraining/inference[14]canreusethelegecyCSIreportdata.
PacketErrorrate(PER):guaranteeminimumSNRlosscomparedwith802.11betoachievethetargetPER(e.g.,1%and/or10%)atagivenMCSinalltypesofchannels
REF_Ref119303329\r\h
[16][15]
.
Computationcomplexity/Latency:minimizetheadditionalcomputationcomplexityorlatencyrequiredbytheAIMLprocess
Potentialfeaturesanalysis
Technicalfeasibilityanalysis
Standardsimpact
UsecaseofCSIfeedbackcompression
Thestandardimpactmayinclude:
Additionalsignaling(e.g.,betweenAPandnon-APSTAs)requiredbyAIMLprocessPlaceholderforadditionaltechnicalfeasibilityanalysis
Technicalfeasibility
UsecaseofCSIfeedbackcompression
Thefollowingmetricswillbestudied:
Dataavailabilityandaccesibility:TherearesomeSTAsthatareabletousethedatatoperformAIMLmodeltrainingand/orinference
REF_Ref119086275\r\h
[15][14]
.Thedatausedformodeltrainingand/orinferenceshallbeaccessiblefortheseSTAs.
AP/edgecomputingbasedAIML:Datamaybecollectedfromnon-APSTAs.Thelegeacy802.11CSIreportsmaybeusedastrainingdata.
DevicecomputingbasedAIML:DatashouldbeavailableatallSTAsthatsupportAIMLprocess.
Hardware/softwarecapability:TheSTAsthatuseAIMLtogeneratetheAIMLenabledCSIfeedbackcompressionshallhavethehardwareandsoftwarecapabilitytosupportAIMLalgorithm(s).
AP/edgecomputingbasedAIML
REF_Ref119085527\r\h
[17][16]
:Extradataandmodel(e.g.,modelparameters)exchangemayberequiredtosupportAP/edgecomputingbasedAIML.However,computationisnotexpectedtobelocatedatAPoredgecomputingresourcesforwhichhighercomputationcapabilitiesisexpected.
DevicecomputingbasedAIML
REF_Ref119085527\r\h
[17][16]
:STAsthatsupportAIMLmayberequiredtohaveextracomputationcapability.Extradataandmodel(e.g.,modelparameters)exchangebetweenSTAsmayalsoberequiredtosupportdevicecomputingbasedAIML.
Summary
References
11-22/597r3:May2022WorkingGroupMotions,May18,2022
IEEE802.11-REVmeD2.0,October2022
IEEEP802.11beD2.2,October2022
802.11-18/0818r3,16SpatialStreamSupportinNextGenerationWLAN
802.11-20/1877r1,16SpatialStreamSupport
802.11-20/1535r66,CompendiumofstrawpollsandpotentialchangestotheSpecificationFrameworkDocumentPart2
802.11-22/1515,Acandidatefeature:M
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