On the decision threshold of eigenvalue ratio detector based on moments of joint and marginal distributions of extreme eigenvalues

Handle URI:
http://hdl.handle.net/10754/562679
Title:
On the decision threshold of eigenvalue ratio detector based on moments of joint and marginal distributions of extreme eigenvalues
Authors:
Shakir, Muhammad Zeeshan; Rao, Anlei; Alouini, Mohamed-Slim ( 0000-0003-4827-1793 )
Abstract:
Eigenvalue Ratio (ER) detector based on the two extreme eigenvalues of the received signal covariance matrix is currently one of the most effective solution for spectrum sensing. However, the analytical results of such scheme often depend on asymptotic assumptions since the distribution of the ratio of two extreme eigenvalues is exceptionally complex to compute. In this paper, a non-asymptotic spectrum sensing approach for ER detector is introduced to approximate the marginal and joint distributions of the two extreme eigenvalues. The two extreme eigenvalues are considered as dependent Gaussian random variables such that their joint probability density function (PDF) is approximated by a bivariate Gaussian distribution function for any number of cooperating secondary users and received samples. The PDF approximation approach is based on the moment matching method where we calculate the exact analytical moments of joint and marginal distributions of the two extreme eigenvalues. The decision threshold is calculated by exploiting the statistical mean and the variance of each of the two extreme eigenvalues and the correlation coefficient between them. The performance analysis of our newly proposed approximation approach is compared with the already published asymptotic Tracy-Widom approximation approach. It has been shown that our results are in perfect agreement with the simulation results for any number of secondary users and received samples. © 2002-2012 IEEE.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Electrical Engineering Program; Communication Theory Lab
Publisher:
Institute of Electrical and Electronics Engineers
Journal:
IEEE Transactions on Wireless Communications
Issue Date:
Mar-2013
DOI:
10.1109/TWC.2012.011513.111486
Type:
Article
ISSN:
15361276
Appears in Collections:
Articles; Electrical Engineering Program; Communication Theory Lab; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorShakir, Muhammad Zeeshanen
dc.contributor.authorRao, Anleien
dc.contributor.authorAlouini, Mohamed-Slimen
dc.date.accessioned2015-08-03T11:01:07Zen
dc.date.available2015-08-03T11:01:07Zen
dc.date.issued2013-03en
dc.identifier.issn15361276en
dc.identifier.doi10.1109/TWC.2012.011513.111486en
dc.identifier.urihttp://hdl.handle.net/10754/562679en
dc.description.abstractEigenvalue Ratio (ER) detector based on the two extreme eigenvalues of the received signal covariance matrix is currently one of the most effective solution for spectrum sensing. However, the analytical results of such scheme often depend on asymptotic assumptions since the distribution of the ratio of two extreme eigenvalues is exceptionally complex to compute. In this paper, a non-asymptotic spectrum sensing approach for ER detector is introduced to approximate the marginal and joint distributions of the two extreme eigenvalues. The two extreme eigenvalues are considered as dependent Gaussian random variables such that their joint probability density function (PDF) is approximated by a bivariate Gaussian distribution function for any number of cooperating secondary users and received samples. The PDF approximation approach is based on the moment matching method where we calculate the exact analytical moments of joint and marginal distributions of the two extreme eigenvalues. The decision threshold is calculated by exploiting the statistical mean and the variance of each of the two extreme eigenvalues and the correlation coefficient between them. The performance analysis of our newly proposed approximation approach is compared with the already published asymptotic Tracy-Widom approximation approach. It has been shown that our results are in perfect agreement with the simulation results for any number of secondary users and received samples. © 2002-2012 IEEE.en
dc.publisherInstitute of Electrical and Electronics Engineersen
dc.subjectCopulaen
dc.subjectcorrelation coefficienten
dc.subjecteigenvalue ratio based detectionen
dc.subjectnon-asymptotic Gaussian approximationen
dc.subjectSpectrum sensingen
dc.titleOn the decision threshold of eigenvalue ratio detector based on moments of joint and marginal distributions of extreme eigenvaluesen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentElectrical Engineering Programen
dc.contributor.departmentCommunication Theory Laben
dc.identifier.journalIEEE Transactions on Wireless Communicationsen
dc.contributor.institutionDepartment of Electrical and Computer Engineering, Texas AandM University at Qatar (TAMUQ), Education City, P.O. Box 23874, Doha, Qataren
dc.contributor.institutionDept. of Electrical and Computer Engineering, University of Texas, Austin, TX, United Statesen
kaust.authorAlouini, Mohamed-Slimen
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