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MassiveMIMO中基于统计信道的波束形成和功率分配.docx

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MassiveMIMO中基于统计信道的波束形成和功率分配 Title:StatisticalChannel-basedBeamformingandPowerAllocationinMassiveMIMO Abstract: MassiveMultiple-InputMultiple-Output(MIMO)hasemergedasapromisingtechnologyforenhancingthecapacityandspectralefficiencyofwirelesscommunicationsystems.OneofthekeychallengesinMassiveMIMOisthedesignofbeamformingandpowerallocationtechniquestoefficientlyutilizethelargenumberofantennasatthebasestation.Thispaperfocusesonstatisticalchannel-basedbeamformingandpowerallocation,whichleveragesthestatisticalcharacteristicsofthechanneltooptimizethesystemperformance.Theobjectiveistomaximizethesum-rateorminimizethepowerconsumptionwhilemaintainingatargetquality-of-service(QoS)attheusers.Variousstatisticalchannel-basedtechniques,includingbeamformingalgorithmsandpowerallocationstrategies,arereviewedandcomparedinthispaper. 1.Introduction MassiveMIMOisatechnologythatemploysalargenumberofantennasatthebasestationtoservemultipleuserssimultaneously.Byexploitingthespatialmultiplexinggain,MassiveMIMOcansignificantlyimprovethesystemcapacityandspectralefficiency.However,theefficientutilizationofthelargeantennaarraypresentsseveralchallenges,suchasexcessiveinteruserinterferenceandhighcomputationalcomplexity.Statisticalchannel-basedtechniquesprovideapracticalsolutionbyutilizingstatisticalpropertiesofthewirelesschannel. 2.StatisticalChannelModeling Thestatisticalmodelofthewirelesschannelplaysavitalroleinstatisticalchannel-basedbeamformingandpowerallocation.Thissectiondiscussesthecommonlyusedstatisticalmodels,includingtheKroneckerandKronecker-blockchannelmodels.Theadvantagesandlimitationsofthesemodelsarepresented,alongwiththemethodsforestimatingthestatisticalparameters. 3.StatisticalBeamformingAlgorithms Statisticalbeamformingalgorithmsexploitthestatisticalknowledgeofthechanneltodesignefficientbeamformingvectors.Theconventionalmethodofbeamformingbasedonthemaximumratiotransmission/combining(MRT/MRC)suffersfromhighcomplexityinMassiveMIMOsystems.Alternatively,statisticalbeamformingalgorithms,su