Show simple item record

dc.contributor.authorManco Vásquez, Julio César 
dc.contributor.authorLázaro Gredilla, Miguel
dc.contributor.authorRamírez García, David
dc.contributor.authorVía Rodríguez, Javier 
dc.contributor.authorSantamaría Caballero, Luis Ignacio 
dc.contributor.otherUniversidad de Cantabriaes_ES
dc.date.accessioned2016-10-26T15:52:30Z
dc.date.available2016-10-26T15:52:30Z
dc.date.issued2014-03
dc.identifier.issn0165-1684
dc.identifier.issn1872-7557
dc.identifier.otherTEC2010-19545-C04-03es_ES
dc.identifier.otherCSD2008-00010es_ES
dc.identifier.urihttp://hdl.handle.net/10902/9393
dc.description.abstractMuch of the recent work on multiantenna spectrum sensing in cognitive radio (CR) networks has been based on generalized likelihood ratio test (GLRT) detectors, which lack the ability to learn from past decisions and to adapt to the continuously changing environment. To overcome this limitation, in this paper we propose a Bayesian detector capable of learning in an efficient way the posterior distributions under both hypotheses. These posteriors summarize, in a compact way, all information seen so far by the cognitive secondary user. Our Bayesian model places priors directly on the spatial covariance matrices under both hypothesis, as well as on the probability of channel occupancy. Specifically, we use inverse-gamma and complex inverse-Wishart distributions as conjugate priors for the null and alternative hypothesis, respectively; and a binomial distribution as the prior for channel occupancy. At each sensing period, Bayesian inference is applied and the posterior for the channel occupancy is thresholded for detection. After a suitable approximation, the posteriors are employed as priors for the next sensing frame, which forms the basis of the proposed Bayesian learning procedure. We also include a forgetting mechanism that allows to operate satisfactorily on time-varying scenarios. The performance of the Bayesian detector is evaluated by simulations and also by means of CR testbed composed of universal radio peripheral (USRP) nodes. Both the simulations and our experimental measurements show that the Bayesian detector outperforms the GLRT in a variety of scenarios.es_ES
dc.description.sponsorshipThe research leading to these results has received funding from the Spanish Government (MIC INN) under Projects TEC2010-19545-C04-03 (COSIMA) and CONSOLIDER-INGENIO 2010 CSD2008-00010 (COMONSENS). It also has been supported by FPI Grant BES-2011-047647.es_ES
dc.format.extent21 p.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rights© 2014, Elsevier. Licensed under the Creative Commons Reconocimiento-NoComercial-SinObraDerivadaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.sourceSignal Processing, 2014, 96, Part B, 228–240es_ES
dc.subject.otherBayesian inferencees_ES
dc.subject.otherBayesian forgettinges_ES
dc.subject.otherCognitive radioes_ES
dc.subject.otherGeneralized likelihood ratio test (GLRT)es_ES
dc.subject.otherMultiantenna spectrum sensinges_ES
dc.titleA Bayesian approach for adaptive multiantenna sensing in cognitive radio networkses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherVersionhttp://dx.doi.org/10.1016/j.sigpro.2013.10.005es_ES
dc.rights.accessRightsopenAccesses_ES
dc.identifier.DOI10.1016/j.sigpro.2013.10.005
dc.type.versionacceptedVersiones_ES


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

© 2014, Elsevier. Licensed under the Creative Commons Reconocimiento-NoComercial-SinObraDerivadaExcept where otherwise noted, this item's license is described as © 2014, Elsevier. Licensed under the Creative Commons Reconocimiento-NoComercial-SinObraDerivada