A Bayesian approach for adaptive multiantenna sensing in cognitive radio networks

Much 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 t...

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Autores: Manco Vásquez, Julio César, Lázaro Gredilla, Miguel, Ramírez García, David, Vía Rodríguez, Javier, Santamaría Caballero, Luis Ignacio|||0000-0003-0040-7436
Tipo de recurso: artículo
Fecha de publicación:2014
País:España
Institución:Universidad de Cantabria (UC)
Repositorio:UCrea Repositorio Abierto de la Universidad de Cantabria
Idioma:inglés
OAI Identifier:oai:repositorio.unican.es:10902/9393
Acceso en línea:http://hdl.handle.net/10902/9393
Access Level:acceso abierto
Palabra clave:Bayesian inference
Bayesian forgetting
Cognitive radio
Generalized likelihood ratio test (GLRT)
Multiantenna spectrum sensing
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spelling A Bayesian approach for adaptive multiantenna sensing in cognitive radio networksManco Vásquez, Julio CésarLázaro Gredilla, MiguelRamírez García, DavidVía Rodríguez, JavierSantamaría Caballero, Luis Ignacio|||0000-0003-0040-7436Bayesian inferenceBayesian forgettingCognitive radioGeneralized likelihood ratio test (GLRT)Multiantenna spectrum sensingMuch 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.The 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.ElsevierUniversidad de Cantabria20142014-03-01journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articlehttp://hdl.handle.net/10902/9393Signal Processing, 2014, 96, Part B, 228–240reponame:UCrea Repositorio Abierto de la Universidad de Cantabriainstname:Universidad de Cantabria (UC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Atribución-NoComercial-SinDerivadas 3.0 Españahttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:repositorio.unican.es:10902/93932026-06-02T12:39:31Z
dc.title.none.fl_str_mv A Bayesian approach for adaptive multiantenna sensing in cognitive radio networks
title A Bayesian approach for adaptive multiantenna sensing in cognitive radio networks
spellingShingle A Bayesian approach for adaptive multiantenna sensing in cognitive radio networks
Manco Vásquez, Julio César
Bayesian inference
Bayesian forgetting
Cognitive radio
Generalized likelihood ratio test (GLRT)
Multiantenna spectrum sensing
title_short A Bayesian approach for adaptive multiantenna sensing in cognitive radio networks
title_full A Bayesian approach for adaptive multiantenna sensing in cognitive radio networks
title_fullStr A Bayesian approach for adaptive multiantenna sensing in cognitive radio networks
title_full_unstemmed A Bayesian approach for adaptive multiantenna sensing in cognitive radio networks
title_sort A Bayesian approach for adaptive multiantenna sensing in cognitive radio networks
dc.creator.none.fl_str_mv Manco Vásquez, Julio César
Lázaro Gredilla, Miguel
Ramírez García, David
Vía Rodríguez, Javier
Santamaría Caballero, Luis Ignacio|||0000-0003-0040-7436
author Manco Vásquez, Julio César
author_facet Manco Vásquez, Julio César
Lázaro Gredilla, Miguel
Ramírez García, David
Vía Rodríguez, Javier
Santamaría Caballero, Luis Ignacio|||0000-0003-0040-7436
author_role author
author2 Lázaro Gredilla, Miguel
Ramírez García, David
Vía Rodríguez, Javier
Santamaría Caballero, Luis Ignacio|||0000-0003-0040-7436
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Universidad de Cantabria
dc.subject.none.fl_str_mv Bayesian inference
Bayesian forgetting
Cognitive radio
Generalized likelihood ratio test (GLRT)
Multiantenna spectrum sensing
topic Bayesian inference
Bayesian forgetting
Cognitive radio
Generalized likelihood ratio test (GLRT)
Multiantenna spectrum sensing
description Much 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.
publishDate 2014
dc.date.none.fl_str_mv 2014
2014-03-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10902/9393
url http://hdl.handle.net/10902/9393
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv Signal Processing, 2014, 96, Part B, 228–240
reponame:UCrea Repositorio Abierto de la Universidad de Cantabria
instname:Universidad de Cantabria (UC)
instname_str Universidad de Cantabria (UC)
reponame_str UCrea Repositorio Abierto de la Universidad de Cantabria
collection UCrea Repositorio Abierto de la Universidad de Cantabria
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.301629

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