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基于改进的稀疏度自适应匹配追踪算法的宽带压缩频谱感知（英文）.docx

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基于改进的稀疏度自适应匹配追踪算法的宽带压缩频谱感知（英文） WidebandCompressedSpectrumSensingBasedonImprovedSparsity-AdaptiveMatchingPursuitTrackingAlgorithm Introduction: CompressedSpectrumSensing(CSS)hasbeenwidelyusedinrecentyearsintheareaofspectrumsensing.Itisatechniquetocompressthereceivedsignalintoalow-dimensionalspace,andthenmeasurethesignal'sactivityinthatspace,whichprovidesanefficientwaytoutilizethelimitedresourcesofradiofrequencyspectrum.However,duetothelimitationsoftheCSSalgorithmitself,thesparsityofthesignalcanbedifficulttomeasureaccuratelyandcanleadtoalowdetectionrateandhighfalsealarmrate. Toaddresstheseissues,thispaperproposesanimprovedCSSalgorithmbasedonasparsity-adaptivematchingpursuittrackingalgorithm,whichcanadaptivelyadjustthesparsitylevelaccordingtothesignal'scharacteristics,therebygreatlyimprovingaccuratedetectionrateandreducingthefalsealarmrate. Methodology: Theproposedalgorithmconsistsofthreemainparts:signalcompression,sparsityestimationandactivitydetection.Firstly,thereceivedsignaliscompressedbyaprojectionmatrix.Then,thesparsityofthecompressedsignalisestimatedbyusingtheMatchingPursuitalgorithm.Thesparsitymeasureisthenusedtoadjustthesparsitylevelofthealgorithmtomatchtheactualsignal'scharacteristics.Finally,theactivefrequenciesaredetectedandtheenergyofthesignalisrecoveredbyinversetransform. Thesparsity-adaptivematchingpursuit(SAMP)algorithmisappliedtoimprovetheestimationaccuracyofsparsityinCSS.SAMPisatrackingalgorithmcomposedofagreedypursuitmethodandaKalmanfilter.Itcanadaptivelyadjustthesparsitylevelbyestimatingthesignal'sactivityinreal-timethroughthemeasurementobtainedfromthecompressedsignal.TheSAMPalgorithmalsohastheabilitytosuppressthenoiseandinterferenceinthemeasurementnoisetoavoidthefalsealarmrateinthesparsityestimationprocess. ResultsandAnalysis: Simulationresultsdemonstratetheeffectivenessoftheproposedalgorithm.ComparedwithtraditionalCSSalgorithms,suchasFixedSparsityMatchingPursuit(FSMP),theproposedsparsity-adaptivematchingpursuit(SAMP)algorithmimprovesthedetectionra