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一种新的短波实时信道估值方法.docx

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一种新的短波实时信道估值方法 Title:ANovelReal-TimeShortwaveChannelEstimationMethod Abstract: Therapidadvancementofwirelesscommunicationtechnologiesinrecentyearshasledtoanincreaseddemandforreliableandefficientchannelestimationmethods.Shortwavecommunication,inparticular,iswidelyusedinmaritimeandaviationindustries,wherethedynamicnatureofthechannelposesachallengetoaccurateestimation.Inthispaper,weproposeanovelreal-timeshortwavechannelestimationmethodthatcombinesmachinelearningtechniqueswithtraditionalsignalprocessingalgorithms.Theproposedmethodaimstoimprovetheaccuracyandefficiencyofchannelestimationinshortwavecommunicationsystems. Keywords:shortwavecommunication,channelestimation,machinelearning,signalprocessing,real-time 1.Introduction Shortwavecommunicationhasgainedsignificantattentionduetoitslong-rangecapabilitiesandsuitabilityforlong-distancecommunication.However,thedynamicnatureofthechannel,characterizedbyfading,interference,andmultipatheffects,posesachallengetoaccuratechannelestimation.Traditionalchannelestimationmethodsoftenrelyonsimplisticassumptionsaboutthechannelandmaynotcaptureitsdynamicbehavioradequately.Therefore,thereisaneedformoresophisticatedandreal-timechannelestimationtechniquesforshortwavecommunicationsystems. 2.RelatedWork Previousworkshaveexploredvarioustechniquesforshortwavechannelestimation,suchasleastsquares,maximumlikelihood,andKalmanfiltering.Whilethesemethodshaveachievedreasonableresults,theyoftendependonknownchannelmodelsorassumptionsthatmaynotholdinreal-worldscenarios.Machinelearningtechniques,ontheotherhand,haveshownpromiseinaddressingtheselimitationsbylearningfromdataandadaptingtochangingchannelconditionsinreal-time. 3.ProposedMethod Ourproposedmethodintegratesmachinelearningalgorithmswithtraditionalsignalprocessingtechniquestoachievereal-timeshortwavechannelestimation.Thekeystepsinvolvedinourmethodareasfollows: 3.1DataCollectionandPreprocessing Webeginbycollectingadatasetofshortwavechannelresponsesundervaryingchannelconditions.Thisdatasetservesasthetra