Bayesian Blind PARAFAC Recievers forDS-CDMA Systems

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dc.citation.bibtexNameinproceedingsen_US
dc.citation.conferenceNameStatistical Signal Processing Workshopen_US
dc.contributor.authorde Baynast, Alexandreen_US
dc.contributor.authorDeclercq, Daviden_US
dc.contributor.authorDe Lathauwer, Lievenen_US
dc.contributor.authorAazhang, Behnaamen_US
dc.contributor.orgCenter for Multimedia Communications (http://cmc.rice.edu/)en_US
dc.date.accessioned2007-10-31T00:41:44Zen_US
dc.date.available2007-10-31T00:41:44Zen_US
dc.date.issued2003-10-01en_US
dc.date.modified2004-04-10en_US
dc.date.note2004-04-09en_US
dc.date.submitted2003-10-01en_US
dc.descriptionConference paperen_US
dc.description.abstractIn this paper an original Bayesian approach for blind detec-tion for Code Division Multiple Access (CDMA) Systems in presence of spatial diversity at the receiver is developed. In the noiseless context, the blind detection/identification problem relies on the canonical decomposition (also re-ferred as Parallel Factor analysis [Sidiropoulos, IEEE SP 00], PARAFAC. The author in [Bro,INCINC 96] pro-poses a suboptimal solution in least-squares sense. How-ever, poor performance are obtained in presence of high noise level. The recently emerged Markov chain Monte Carlo (MCMC) signal processing method provide a novel paradigm for tackling this problem. Simulation results are presented to demonstrate the effectiveness of this method.en_US
dc.identifier.citationA. de Baynast, D. Declercq, L. De Lathauwer and B. Aazhang, "Bayesian Blind PARAFAC Recievers forDS-CDMA Systems," 2003.en_US
dc.identifier.urihttps://hdl.handle.net/1911/19834en_US
dc.language.isoengen_US
dc.subjectPARAFACen_US
dc.subject.keywordPARAFACen_US
dc.titleBayesian Blind PARAFAC Recievers forDS-CDMA Systemsen_US
dc.typeConference paperen_US
dc.type.dcmiTexten_US
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