2025通感融合的思考

Nanjing,China,16April BackgroundandMotivations CT-basedISAC IT-basedISAC ConclusionsEvolutionEvolutionofMobileetworks:Datahuman-human

extendedMobileBroadband(faster)高速率，更快1

massiveMachine-Typehuman-thing

UltraReliableLowLatencyComm.超低EvolutionEvolutionofMobileGNetworks:

Comm.

Under

Comm.

Comm.Networks:6G-M.Latva-aho,K.Lepp?nen(eds.),“KeyDriversandResearchChallengesfor6GUbiquitousWirelessIntelligence”,6GFlagship,September2019.

IMT-20306GApplicationIntegratedBaseStation(BS)

ISAC Commsgohigherfrequencyspectrumleadingtoclash,thus Hardware/cost/energy/performanceefficient通信角度計(jì)算角度二者聯(lián) BackgroundandMotivations CT-basedISAC IT-basedISAC ConclusionsPolarisationPolarisationCodingassistedbyState-of-the-State-of-the-separateComm&Sensing

higherKPIfor6G

BSsendingISACwaveforms(t)towardsBSperformestimation

Ourbasedontheecho

r(t)BSdesignsthecodingstructureforthenextTS(timeslot)basedontheinformationoftheechoreceivedinthecurrentTSIntelligence,YZeng,LXiang,KYang.“APolarisationCodingSchemeBasedonAnIntegratedSensingandCommunicationSystem”JournalofInformationandIntelligence,PolarisationPolarisationCodingassistedbyBenchmark:CommDifferentdecodingHigherBenchmark:CommDifferentdecodingHigherthanlowerboundoverimperfectchannelBenchmark:CommDifferentdecodingFig.Sum-rateoverdifferenttime

Fig.BER Fig.Sum-rateoverdifferenttimeIndensehigh-speedscenario,theCommQoSisguranteed,evenintheimperfectscenario.Intheperfectscenario,Indensehigh-speedscenario,theCommQoSisguranteed,evenintheimperfectscenario.Intheperfectscenario,commperformance(BER)isimprovedby6dB,vscommonlyIntheimperfectscenario,commperformance(BER)isimprovedby7dB.AdaptiveAdaptiveModulationassistedbymodulationbasedonechosignal

PilotsignalisalmosteliminatedtoimprovetheChannelestimationiseliminatedtoreducethecomputingresourceIncreasesthemaximumcapacitywhileensuringLiB,XiangL,HuJ,YangK.基于通感一體化技術(shù)的自適應(yīng)調(diào)制方案[J].無(wú)線電通信技術(shù), AdaptiveAdaptiveModulationassistedbyImperfectchannelmodulation ImperfectchannelinstantaneousAsavehiclemoves,allmethodsachieveoptimalmodulationschemeselectioninmostcases.TraditionalmethodsselectahighermodulationschemeatlowerSNRsnearthecriticalswitchingpoint,resultinginhigherBER.DRLmethodsadoptsaconservativepolicynearthecriticalpointtoensurethereliabilityofthechannel.–健壯性強(qiáng)PerformancedegradesasvalueincreasesAverageSE(spectrumefficiency)ofDRLPerformancedegradesasvalueincreasesAverageSE(spectrumefficiency)ofDRLishigherthanthetraditionalBERoftraditionalschemeincreasesasKvalueincreases(NLOS↑)BERofDRLschemedegradesasvalueincreases(moreThedifferencebetweentheBERoftraditionalschemeandDRLschemeincreaseasKvalueincreasesGreenGreenBeamformingDesignforComm.

Beamformingdesignformultipleusersandradartargets. Target:1)tomeetdifferentcomm.demandofcomm.users;tosatisfythedemandofradarminimizethesystemtransmissionThemetricsfor=∑sk+Radar

Themetricsfor Radaris

在p

CommisL.Xiang,K.Xu,J.HuandK.Yang,"GreenBeamformingDesignforIntegratedSensingandCommunicationSystems:APracticalApproachUsingBeam-MatchingErrorMetrics,"inIEEETransactionsonVehicularTechnology,2023 GreenGreenBeamformingDesignforOptimization

mintransmissionpowerComm.constraintsRadarconstraintsTheTheoriginalnon-convexproblemP1EmployingEmployingtheSDR，P1canbeconvertedtoaconvexproblemP2OptimalOptimalTheIRM(Iterativeminimization)isutilizedforrank-onerecoveryGreenGreenBeamformingDesignforOptimal BeampatternofPowerdegradesasNincreases(No.ofantennas)TheincreaseofNleadstoagreaterconcentrationofpowerSatisfythecommunication(20°)andtheradarPowerdegradesasNincreases(No.ofantennas)TheincreaseofNleadstoagreaterconcentrationofpowerTheIRMdoesn’taffectthesystemTheradarmeetsthe GreenGreenBeamformingDesignforCompare(a)and(b):Compare(a)and(b):Ifthereisonlyonecommunicationuserandoneradaruser,theradarcomponentshowstheadvantageinpare(b)and(d):Thesmallertheintervalbetweencommunicationusers,themorepowerisused.PowerPowerAllocationinSensing-aided Target:1)tomeetdifferencecomm.demandofvehicles;2)toconstructdynamictopologyofvehicles OTFS(orthogonaltimefrequencyspace)modulation:suitableformobileNOMA(non-orthogonalmultipleaccess):higherspectrumefficiency,5G/6G

KeXu,LXiang,J.Hu,K.Yang.“OTFS-basedPowerAllocationinISAC

(max(1+

TelecomScienceChina2022BSsendsISACsignalBSsendsISACsignalimplementingdownlinkVehiclesreflectsecholocationestimationPerfectDataPerfectPowerofUE1, Two-userNOMApowerallocation: MMF:max-minfairness(P1) SR:sumrate PerfectchannelvsimperfectPowerPowerAllocationinSensing-aided(1)Imperfectchannel (2)ImperfectchannelPerformancedegradesasKvalueincreases(moreNLOS)DataratesofUE1andUE2staythesame(fairness)SensingandNOMAincreasessystemperformance

Performancedegradesasvalueincreases(moreDataratesofUE1andUE2aredifferentbutthesumrateismorethanMMFRISRIS(智能超表面DesignforLuoLuoC,HuJ,XiangL,etal.ReconfigurableIntelligentSensingSurfaceaidedWirelessPoweredCommunicationNetworks:ASensing-Then-ReflectingApproach[J].IEEETransactionsonCommunications,2023.

Num.ofpassive RISS-UEsRician

Rician

Theclosed-formexpressionofErgodicSpectralCapacityRatiobetweenMISOandSISOsystem

Antennasnum.ofE和I的性能隨著?增加（更強(qiáng)的LoS）和ISS之間的LoS徑存在才能發(fā)揮多天線性能對(duì)于為的信道場(chǎng)景，單天線和多天線性能相同當(dāng)≥1當(dāng)≥1且RIS單元數(shù)足夠大時(shí)，fullCSI方案Sensing-aidedSensing-aidedComm.UsingMultipleTheplacementforinterferenceeliminationLuoC,HuJ,XiangL,etal.OptimizingPlacementandPowerAllocationinReconfigurableIntelligentSensingSurfacesforEnhancedSensingandCommunicationPerformance.SubmittedtoTVT.ThemaximizationofThemaximizationofSensingdetectablerangeRISS-aidedRISS-aidedISACforMulti-

LuoC,HuJ,XiangL,etal.ReconfigurableIntelligentSensingSurfaceenablesCapacityMaximizationofWPCNs:InterferenceSuppressionandMassiveWirelessEnergyTransfer.Manuscript.

TherobustdesignforinterferencesuppressionImplementationImplementationofSensing-aidedTarget:TosenseTxinformation(DOA)anduseittodesignareflectionphaseshiftmatrixoftheRISSfortheRx.2-bit

Hornantenna:3.5GHz,10dBigain;4sensingantennas:3.5GHz,0.688wavelength 2-bitRISS:3.5GHz,100passiveelementsand4activesensingantennas USRP:X300,3.5GHz,single-carrier GPSDO:10MHzSC-CutHighStabilityOvenControlledCrystalOscillator(OCXO)ImplementationImplementationofSensing-aided ImplementationImplementationofSensing-aidedOriginalconstellation:RISS-aidedconstellation: BackgroundandMotivations CT-basedISAC IT(Vison)-basedISACChannelEstimation/DataAugmentationUsingImages–通過虛擬ResourceAllocationUsingReal-lifeCameraImages-通過真實(shí)圖WirelessChargingVehiclesUsingaCombinationofVisionandRF–通過視覺和射頻聯(lián)合實(shí)現(xiàn)數(shù)能小車 ConclusionsChannelChannelEstimationUsingImage

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