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一种改进StOMP的MassiveMIMO下行信道估计方法.docx

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一种改进StOMP的MassiveMIMO下行信道估计方法 Title:AnImprovedMassiveMIMODownlinkChannelEstimationMethodbasedonStOMP Abstract: MassiveMIMO(Multiple-InputMultiple-Output)isapromisingtechnologyforachievinghighdataratesandenergyefficiencyinfuturewirelesscommunicationsystems.However,accuratechannelestimationplaysacrucialroleinrealizingthebenefitsofMassiveMIMO.Inthispaper,weproposeanimproveddownlinkchannelestimationmethodbasedontheStOMP(SparseTomographicMatchingPursuit)algorithm.TheproposedmethodaimstoenhancetheaccuracyandefficiencyofMassiveMIMOdownlinkchannelestimation,takingintoaccountthespecificcharacteristicsofMassiveMIMOsystems.Simulationresultsdemonstratethesuperiorityoftheproposedmethodoverexistingchannelestimationtechniquesintermsofestimationaccuracyandsystemperformance. 1.Introduction MassiveMIMOisakeytechnologyinnext-generationwirelesscommunicationsystems,wherebasestationsareequippedwithalargenumberofantennastoservemultipleuserssimultaneously.Byutilizingthespatialdegreesoffreedomprovidedbymassiveantennaarrays,MassiveMIMOcansignificantlyincreasesystemcapacityandimprovespectralefficiency.However,channelestimationinMassiveMIMOsystemsposesseveralchallengesduetothelargenumberofantennas,limitedpilotresources,andinterferences.Thispaperpresentsanimprovedapproachtoaddressthesechallengesandenhancetheaccuracyandefficiencyofdownlinkchannelestimation. 2.RelatedWork SeveralchannelestimationtechniqueshavebeenproposedforMassiveMIMOsystems,includingleastsquares(LS),minimummeansquareerror(MMSE),andcompressivesensing(CS)basedmethods.Whilethesemethodshaveshownpromisingresults,theysufferfromlimitationssuchashighcomputationalcomplexityorinefficientutilizationofpilotresources.Toovercometheselimitations,theStOMPalgorithm,arecentlydevelopedsparsechannelestimationtechnique,canbeemployedasapromisingalternative. 3.SystemModel TheproposedmethodconsidersadownlinkscenarioinaMassiveMIMOsystem,whereabasestationequippedwithMantennasservesmultiplesingle-antennauserssimultaneously.Thereceivedsignalmodelinthetime