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Hybrid Higher-Order Statistics Learning in Multiuser Detection

dc.contributor.authorCaamaño, Antonio J.
dc.contributor.authorBoloix-Tortosa, R.
dc.contributor.authorRamos, Javier
dc.contributor.authorMurillo-Fuentes, Juan J.
dc.date.accessioned2009-01-30T15:14:44Z
dc.date.available2009-01-30T15:14:44Z
dc.date.issued2004-11
dc.identifier.citationIEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS¿PART C: APPLICATIONS AND REVIEWS, VOL. 34, NO. 4, NOVEMBER 2004, pp. 417-424es
dc.identifier.urihttp://hdl.handle.net/10115/1872
dc.description.abstractIn this paper, we explore the significance of second- and higher-order statistics learning in communication systems. The final goal in spread-spectrum communication systems is to receive a signal of interest completely free from interference caused by other concurrent signals. To achieve this end, we exploit the structure of the interference by designing second-order statistics detectors, such as the minimum square error, in conjunction with higher-order statistics (HOS) techniques, such as the blind source separation (BSS). This hybrid higher-order statistics (HyHOS) approach enables us to alleviate BSS algorithms of one of their main problems, that is, their sensitiveness to high levels of noise. In addition, we benefit from remarkable properties of BSS in learning such as fast learning (superefficiency) and independence of the initial settings of the problem (equivariance). We successfully applied the results of this approach to the design of multiuser detectors in code-division multiple access channels.es
dc.language.isoenges
dc.publisherInstitute of Electrical and Electronical Engineeringes
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectTelecomunicacioneses
dc.titleHybrid Higher-Order Statistics Learning in Multiuser Detectiones
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.subject.unesco3325 Tecnología de las Telecomunicacioneses
dc.description.departamentoTeoría de la Señal y Comunicaciones


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