Blind phase noise estimation and data detection based on SMC technique and unscented filtering

dc.contributor.authorPanayırcı, Erdal
dc.contributor.authorÇırpan, Hakan Ali
dc.contributor.authorMoeneclaey, Marc E.
dc.contributor.authorNoels, Nele
dc.date.accessioned2021-02-18T20:43:39Z
dc.date.available2021-02-18T20:43:39Z
dc.date.issued2006
dc.description.abstractIn this paper, a computationally efficient algorithm is presented for tracing phase noise with linear drift and blind data detection jointly, based on a sequential Monte Carlo(SMC) method. Tracing of phase noise is achieved by Kalman filter and the nonlinearity of the observation process is taken care of by unscented filter rather that using extended Kalman technique. On the other hand,SMC method treats the transmitted symbols as "missing data" and draw samples sequentially of them based on the observed signal samples up to time t. This way, the Bayesian estimates of the phase noise and the incoming data are obtained through these samples, sequentially drawn, together with their importance weights. The proposed receiver structure is seen to be ideally suited for high-speed parallel implementation using VLSI technology.en_US
dc.description.sponsorshipISL Altran,Galileo Avionics,Selex Sistemi Intergrati,STMicroelectronics,University of Pisaen_US
dc.identifier.citation2
dc.identifier.issn2219-5491en_US
dc.identifier.issn2219-5491
dc.identifier.scopus2-s2.0-84862609161en_US
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/20.500.12469/3948
dc.identifier.wosqualityN/A
dc.institutionauthorPanayırcı, Erdalen_US
dc.language.isoenen_US
dc.relation.journalEuropean Signal Processing Conferenceen_US
dc.relation.publicationcategoryKitap Bölümü - Uluslararasıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBayesian estimateen_US
dc.subjectComputationally efficienten_US
dc.subjectData detectionen_US
dc.subjectParallel implementationsen_US
dc.titleBlind phase noise estimation and data detection based on SMC technique and unscented filteringen_US
dc.typeBook Parten_US
dspace.entity.typePublication

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