Eigenvalue ratio detection based on exact moments of smallest and largest eigenvalues

Handle URI:
http://hdl.handle.net/10754/564346
Title:
Eigenvalue ratio detection based on exact moments of smallest and largest eigenvalues
Authors:
Shakir, Muhammad; Tang, Wuchen; Rao, Anlei; Imran, Muhammad Ali; Alouini, Mohamed-Slim ( 0000-0003-4827-1793 )
Abstract:
Detection based on eigenvalues of received signal covariance matrix is currently one of the most effective solution for spectrum sensing problem in cognitive radios. However, the results of these schemes always depend on asymptotic assumptions since the close-formed expression of exact eigenvalues ratio distribution is exceptionally complex to compute in practice. In this paper, non-asymptotic spectrum sensing approach to approximate the extreme eigenvalues is introduced. In this context, the Gaussian approximation approach based on exact analytical moments of extreme eigenvalues is presented. In this approach, the extreme eigenvalues are considered as dependent Gaussian random variables such that the joint probability density function (PDF) is approximated by bivariate Gaussian distribution function for any number of cooperating secondary users and received samples. In this context, the definition of Copula is cited to analyze the extent of the dependency between the extreme eigenvalues. Later, the decision threshold based on the ratio of dependent Gaussian extreme eigenvalues is derived. The performance analysis of our newly proposed approach is compared with the already published asymptotic Tracy-Widom approximation approach. © 2011 ICST.
KAUST Department:
Physical Sciences and Engineering (PSE) Division; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Electrical Engineering Program; Communication Theory Lab
Publisher:
Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (ICST)
Journal:
Proceedings of the 6th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications
Conference/Event name:
2011 6th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications, CROWNCOM 2011
Issue Date:
2011
DOI:
10.4108/icst.crowncom.2011.246151
Type:
Conference Paper
ISBN:
9781936968190
Appears in Collections:
Conference Papers; Physical Sciences and Engineering (PSE) Division; Electrical Engineering Program; Communication Theory Lab; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorShakir, Muhammaden
dc.contributor.authorTang, Wuchenen
dc.contributor.authorRao, Anleien
dc.contributor.authorImran, Muhammad Alien
dc.contributor.authorAlouini, Mohamed-Slimen
dc.date.accessioned2015-08-04T06:24:24Zen
dc.date.available2015-08-04T06:24:24Zen
dc.date.issued2011en
dc.identifier.isbn9781936968190en
dc.identifier.doi10.4108/icst.crowncom.2011.246151en
dc.identifier.urihttp://hdl.handle.net/10754/564346en
dc.description.abstractDetection based on eigenvalues of received signal covariance matrix is currently one of the most effective solution for spectrum sensing problem in cognitive radios. However, the results of these schemes always depend on asymptotic assumptions since the close-formed expression of exact eigenvalues ratio distribution is exceptionally complex to compute in practice. In this paper, non-asymptotic spectrum sensing approach to approximate the extreme eigenvalues is introduced. In this context, the Gaussian approximation approach based on exact analytical moments of extreme eigenvalues is presented. In this approach, the extreme eigenvalues are considered as dependent Gaussian random variables such that the joint probability density function (PDF) is approximated by bivariate Gaussian distribution function for any number of cooperating secondary users and received samples. In this context, the definition of Copula is cited to analyze the extent of the dependency between the extreme eigenvalues. Later, the decision threshold based on the ratio of dependent Gaussian extreme eigenvalues is derived. The performance analysis of our newly proposed approach is compared with the already published asymptotic Tracy-Widom approximation approach. © 2011 ICST.en
dc.publisherInstitute for Computer Sciences, Social Informatics and Telecommunications Engineering (ICST)en
dc.subjectCopulaen
dc.subjecteigenvalue ratio based detectionen
dc.subjectnon-asymptotic Gaussian approximationen
dc.subjectSpectrum sensingen
dc.titleEigenvalue ratio detection based on exact moments of smallest and largest eigenvaluesen
dc.typeConference Paperen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentElectrical Engineering Programen
dc.contributor.departmentCommunication Theory Laben
dc.identifier.journalProceedings of the 6th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communicationsen
dc.conference.date1 June 2011 through 3 June 2011en
dc.conference.name2011 6th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications, CROWNCOM 2011en
dc.conference.locationOsakaen
dc.contributor.institutionCentre for Communication Systems Research (CCSR), University of Surrey Guildford GU1 7XH, Surrey, United Kingdomen
kaust.authorShakir, Muhammaden
kaust.authorRao, Anleien
kaust.authorAlouini, Mohamed-Slimen
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