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基于GP分布拟合检验的BPSKQPSK信号调制识别.docx

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基于GP分布拟合检验的BPSKQPSK信号调制识别 Title:ModulationRecognitionofBPSKandQPSKSignalsusingGoodness-of-FitTestbasedonGaussianProcessDistribution Abstract: Modulationrecognitionplaysacrucialroleinvariouscommunicationsandsignalprocessingapplications.Inthispaper,weproposeanovelapproachtoidentifythemodulationtypeofBPSK(BinaryPhaseShiftKeying)andQPSK(QuadraturePhaseShiftKeying)signalsbasedonthegoodness-of-fittestusingtheGaussianProcess(GP)distribution.TheGPdistributioniscapableofmodelingthestatisticalcharacteristicsofvariousmodulationschemes,makingitanidealcandidateformodulationrecognitiontasks. Introduction: Modulationrecognitionistheprocessofidentifyingthemodulationschemeusedtotransmitinformationinacommunicationsystem.Withtherapiddevelopmentofwirelesscommunicationsystems,accurateandefficientmodulationrecognitionalgorithmsareessentialforreliableandrobustsignalprocessing.BPSKandQPSKarewidelyusedmodulationschemesindigitalcommunicationsystemsduetotheirsimplicityandspectralefficiency.Hence,developingarobustandaccuratemodulationrecognitiontechniquespecificallyforBPSKandQPSKsignalsisofgreatimportance. Methodology: Theproposedapproachutilizesthegoodness-of-fittestbasedontheGPdistributiontodistinguishbetweenBPSKandQPSKsignals.TheGPdistributionisaflexiblenonparametricdistributionmodelthatcancapturethestatisticalcharacteristicsofawiderangeofdata.ByfittingtheobservedsignaltotheGPdistribution,wecanassessthesimilaritybetweentheobservedsignalandtheexpectedsignaldistributionforeachmodulationscheme. 1.Preprocessing: Thereceivedsignalispreprocessedtoremovenoiseandunwantedinterference.Itinvolvesfiltering,demodulation,andsynchronizationprocessestoensureaccuratemodulationrecognition. 2.GaussianProcessModel: TheGPmodelistrainedusingknownBPSKandQPSKsignalstocapturetheirrespectivestatisticalcharacteristics.TheGPmodelisanon-parametricprobabilisticmodelthatcanadaptivelylearnandgeneralizefromatrainingdataset.Itallowsustoestimatetheunderlyingprobabilitydistributionfunctionofthesignal. 3.Goodness-of-FitTest: T