Blind data detection in the presence of PLL phase noise by sequential Monte Carlo method

dc.citation.epage3206en_US
dc.citation.spage3202en_US
dc.contributor.authorPanayırcı, Erdalen_US
dc.contributor.authorÇırpan H. A.en_US
dc.contributor.authorMoeneclaey, M.en_US
dc.contributor.authorNoels, N.en_US
dc.coverage.spatialIstanbul, Turkey
dc.date.accessioned2016-02-08T11:47:18Z
dc.date.available2016-02-08T11:47:18Z
dc.date.issued2006-06en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionDate of Conference: 11-15 June 2006
dc.descriptionConference name: 2006 IEEE International Conference on Communications
dc.description.abstractIn this paper, based on a sequential Monte Carlo method, a computationally efficient algorithm is presented for blind data detection in the presence of residual phase noise generated at the output the phase tracking loop employed in a digital receiver. The basic idea is to treat 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. © 2006 IEEE.en_US
dc.identifier.doi10.1109/ICC.2006.255299en_US
dc.identifier.issn0536-1486
dc.identifier.urihttp://hdl.handle.net/11693/27198
dc.language.isoEnglishen_US
dc.publisherIEEE
dc.relation.isversionofhttp://dx.doi.org/10.1109/ICC.2006.255299en_US
dc.source.title2006 IEEE International Conference on Communicationsen_US
dc.subjectAlgorithmsen_US
dc.subjectCodes (symbols)en_US
dc.subjectMonte Carlo methodsen_US
dc.subjectPhase noiseen_US
dc.subjectBlind data detectionen_US
dc.subjectVLSI technologyen_US
dc.subjectPhase locked loopsen_US
dc.titleBlind data detection in the presence of PLL phase noise by sequential Monte Carlo methoden_US
dc.typeConference Paperen_US
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