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1、Thisarticlehasbeenacceptedforinclusioninafutureissueofthisjournal.Contentisfinalaspresented,withtheexceptionofpagination.IEEETRANSACTIONSONCOMMUNICATIONS,ACCEPTEDFORPUBLICATION1SpectrumSensingAlgorithmsviaFiniteRandomMatricesWenshengZhang,StudentMember,IEEE,GiuseppeAbreu,Se
2、niorMember,IEEE,MamikoInamori,Member,IEEE,andYukitoshiSanada,Member,IEEEAbstract—WeaddressthePrimaryUser(PU)detection(spec-fundamentaltoenableefficientautonomous(cognitive)utiliza-trumsensing)problem,relevanttocognitiveradio,fromafinitetionofspectrumbymultipleradios.Thisprobl
3、em,referredrandommatrixtheoretical(RMT)perspective.Specifically,wetoasspectrumsensingisthereforeofmajorimportanceinemployrecently-derivedclosed-formandexactexpressionsforthefieldofwirelesscommunicationsandatthecoreofthethedistributionofthestandardconditionnumber(SCN)ofuncorre
4、latedandsemi-correlatedrandomdualcentralWishartcognitiveradioparadigm[1]–[3].matricesoffinitesizesinthedesignHypothesis-Testingalgo-IthasbeenrecentlyshownthatspectrumsensingalgorithmsrithmstodetectthepresenceofPUsignals.Inparticular,twoofsuperiorperformanceandrobustnesscanbe
5、designedusingalgorithmsaredesigned,withbasisontheSCNdistributioninthetheeigenvaluesofWishartrandommatricesconstructedfromabsence(ℋ0)andinthepresence(ℋ1)ofPUsignals,respectively.DuetoaninherentpropertyoftheSCN’s,theℋ0testrequireschannelsamples[4]–[7].noestimationofSNRoranyot
6、herinformationonthePUIn[4],forinstance,spectrumsensingmethodswerepro-signal,whiletheℋ1testrequiresSNRonly.Furtherattractiveposedwhichrelyonthepropertythattheeingenvaluesofadvantagesofthenewtechniquesare:?)duetotheaccuracyof?×?Wishartrandommatrices,with(?,?)→∞andathefiniteSCN
7、distributions,superiorperformanceisachievedunderafinitenumberofsamples,comparedtoasymptoticconstantaspectratio?≜?/?,followtheMarchenko-PasturRMT-basedalternatives;?)sinceexpressionstomodeltheSCN(MP)law[8].TheMPlawestablishesthatthelargest(??)andstatisticsbothintheabsenceandp
8、resenceofPUsignalaresmallest(?1)eigenvaluesoflargematricesconverge–underused,thest