GLRT-based spectrum sensing for cognitive radio with prior information

We consider the spectrum sensing problem in cognitive radio networks. We offer a framework for optimal joint detection and parameter estimation when the secondary users have only a small number of signal samples. We discuss the finite-sample optimality of the generalized likelihood ratio test (GLRT)...

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Detalles Bibliográficos
Autores: Font Segura, Josep, Wang, Xiaodong
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2010
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/58837
Acceso en línea:http://hdl.handle.net/10230/58837
http://dx.doi.org/10.1109/TCOMM.2010.07.090556
Access Level:acceso abierto
Palabra clave:Cognitive radio
Spectrum sensing
Generalized likelihood ratio test (GLRT)
Prior information
OFDMA
MIMO
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spelling GLRT-based spectrum sensing for cognitive radio with prior informationFont Segura, JosepWang, XiaodongCognitive radioSpectrum sensingGeneralized likelihood ratio test (GLRT)Prior informationOFDMAMIMOWe consider the spectrum sensing problem in cognitive radio networks. We offer a framework for optimal joint detection and parameter estimation when the secondary users have only a small number of signal samples. We discuss the finite-sample optimality of the generalized likelihood ratio test (GLRT) and derive the corresponding GLRT spectrum sensing algorithms by exploiting the statistics of the received signal and the prior information on the channel, noise, as well as the data signal. An iterative GLRT sensing algorithm, and a simple non-iterative GLRT sensing algorithm are developed for slow and fast-fading channels, respectively, with the latter also serving as an approximate sensing method for slow-fading channels. The proposed techniques are also extended for spectrum sensing in orthogonal frequency-division multiple-access (OFDMA) systems and in multiple-input multiple-output (MIMO) systems. It is seen that the proposed simple non-iterative fast-fading GLRT sensing algorithm offers the best performance in all systems under considerations, including slow fading channels, fast fading channels, OFDMA systems, and MIMO systems, and it significantly outperforms several state-of-the-art spectrum sensing methods in these systems when there is noise uncertainty.Institute of Electrical and Electronics Engineers (IEEE)202420242010info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/58837http://dx.doi.org/10.1109/TCOMM.2010.07.090556reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésIEEE Transactions on Communications. 2010;58(7):2137-46© 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. http://dx.doi.org/10.1109/TCOMM.2010.07.090556info:eu-repo/semantics/openAccessoai:recercat.cat:10230/588372026-05-29T05:05:01Z
dc.title.none.fl_str_mv GLRT-based spectrum sensing for cognitive radio with prior information
title GLRT-based spectrum sensing for cognitive radio with prior information
spellingShingle GLRT-based spectrum sensing for cognitive radio with prior information
Font Segura, Josep
Cognitive radio
Spectrum sensing
Generalized likelihood ratio test (GLRT)
Prior information
OFDMA
MIMO
title_short GLRT-based spectrum sensing for cognitive radio with prior information
title_full GLRT-based spectrum sensing for cognitive radio with prior information
title_fullStr GLRT-based spectrum sensing for cognitive radio with prior information
title_full_unstemmed GLRT-based spectrum sensing for cognitive radio with prior information
title_sort GLRT-based spectrum sensing for cognitive radio with prior information
dc.creator.none.fl_str_mv Font Segura, Josep
Wang, Xiaodong
author Font Segura, Josep
author_facet Font Segura, Josep
Wang, Xiaodong
author_role author
author2 Wang, Xiaodong
author2_role author
dc.subject.none.fl_str_mv Cognitive radio
Spectrum sensing
Generalized likelihood ratio test (GLRT)
Prior information
OFDMA
MIMO
topic Cognitive radio
Spectrum sensing
Generalized likelihood ratio test (GLRT)
Prior information
OFDMA
MIMO
description We consider the spectrum sensing problem in cognitive radio networks. We offer a framework for optimal joint detection and parameter estimation when the secondary users have only a small number of signal samples. We discuss the finite-sample optimality of the generalized likelihood ratio test (GLRT) and derive the corresponding GLRT spectrum sensing algorithms by exploiting the statistics of the received signal and the prior information on the channel, noise, as well as the data signal. An iterative GLRT sensing algorithm, and a simple non-iterative GLRT sensing algorithm are developed for slow and fast-fading channels, respectively, with the latter also serving as an approximate sensing method for slow-fading channels. The proposed techniques are also extended for spectrum sensing in orthogonal frequency-division multiple-access (OFDMA) systems and in multiple-input multiple-output (MIMO) systems. It is seen that the proposed simple non-iterative fast-fading GLRT sensing algorithm offers the best performance in all systems under considerations, including slow fading channels, fast fading channels, OFDMA systems, and MIMO systems, and it significantly outperforms several state-of-the-art spectrum sensing methods in these systems when there is noise uncertainty.
publishDate 2010
dc.date.none.fl_str_mv 2010
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/58837
http://dx.doi.org/10.1109/TCOMM.2010.07.090556
url http://hdl.handle.net/10230/58837
http://dx.doi.org/10.1109/TCOMM.2010.07.090556
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv IEEE Transactions on Communications. 2010;58(7):2137-46
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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