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基于深度学习的大规模MIMO信道状态信息反馈.docx

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基于深度学习的大规模MIMO信道状态信息反馈 Abstract Massivemultiple-inputmultiple-output(MIMO)isapromisingtechnologytoimprovethecapacityandreliabilityofwirelesscommunicationsystems.ToexploitthefullpotentialofmassiveMIMOsystems,accurateandtimelychannelstateinformation(CSI)feedbackisessential.However,thetraditionalCSIfeedbackmethodsarelimitedbytheoverheadandfeedbackdelay,especiallyinlarge-scaleMIMOsystems.Inrecentyears,deeplearningtechniqueshavebeenappliedtoovercometheselimitations.Thispaperprovidesanoverviewofthedeeplearning-basedCSIfeedbackformassiveMIMOsystems.WereviewthebasicconceptsandprinciplesofdeeplearninganditsapplicationtoCSIfeedback.Wealsodiscussthechallengesandfutureresearchdirectionsinthisfield. Introduction MassiveMIMOisakeytechnologyforfuturewirelesscommunicationsystemsduetoitspotentialtoincreasethecapacityandreliabilityofwirelessnetworks.InamassiveMIMOsystem,multipleantennasaredeployedatthebasestation(BS)andtheuserequipment(UE)toenablespatialmultiplexing,spatialdiversity,andinterferencesuppression.However,inordertofullyexploitthebenefitsofmassiveMIMO,accurateandtimelychannelstateinformation(CSI)feedbackisrequired.CSIfeedbackallowstheBStoadaptthetransmissionschemetothechannelconditionsinreal-timeandthusmaximizethespectralefficiencyofthesystem. ThetraditionalCSIfeedbackmethodsarebasedontrainingsequences,pilotsignals,andchannelestimationalgorithms.However,thesemethodssufferfromhighoverheadandfeedbackdelay,whichbecomemoresevereinlarge-scaleMIMOsystems.Toovercometheselimitations,variousdeeplearningtechniqueshavebeenproposedandappliedtoCSIfeedbackinrecentyears. Deeplearningisasubfieldofmachinelearningthatisbasedonartificialneuralnetworks(ANNs)withmultiplelayers.TheANNsaretrainedtoextractandlearnthefeaturesandpatternsfromtheinputdatawithoutanypriorknowledgeaboutthedatadistribution.Deeplearninghasdemonstratedimpressiveperformanceinvariousapplications,suchasimagerecognition,naturallanguageprocessing,andspeechrecognition. Inthispaper,weprovideanoverviewofthedeeplearning-basedCSIfee