Collaborative spectrum sensing based on the ratio between largest eigenvalue and Geometric mean of eigenvalues

Handle URI:
http://hdl.handle.net/10754/564467
Title:
Collaborative spectrum sensing based on the ratio between largest eigenvalue and Geometric mean of eigenvalues
Authors:
Shakir, Muhammad; Rao, Anlei; Alouini, Mohamed-Slim ( 0000-0003-4827-1793 )
Abstract:
In this paper, we introduce a new detector referred to as Geometric mean detector (GEMD) which is based on the ratio of the largest eigenvalue to the Geometric mean of the eigenvalues for collaborative spectrum sensing. The decision threshold has been derived by employing Gaussian approximation approach. In this approach, the two random variables, i.e. The largest eigenvalue and the Geometric mean of the eigenvalues are considered as independent Gaussian random variables such that their cumulative distribution functions (CDFs) are approximated by a univariate Gaussian distribution function for any number of cooperating secondary users and received samples. The approximation approach is based on the calculation of exact analytical moments of the largest eigenvalue and the Geometric mean of the eigenvalues of the received covariance matrix. The decision threshold has been calculated by exploiting the CDF of the ratio of two Gaussian distributed random variables. In this context, we exchange the analytical moments of the two random variables with the moments of the Gaussian distribution function. The performance of the detector is compared with the performance of the energy detector and eigenvalue ratio detector. Analytical and simulation results show that our newly proposed detector yields considerable performance advantage in realistic spectrum sensing scenarios. Moreover, our results based on proposed approximation approach are in perfect agreement with the empirical results. © 2011 IEEE.
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 of Electrical and Electronics Engineers (IEEE)
Journal:
2011 IEEE GLOBECOM Workshops (GC Wkshps)
Conference/Event name:
2011 IEEE GLOBECOM Workshops, GC Wkshps 2011
Issue Date:
Dec-2011
DOI:
10.1109/GLOCOMW.2011.6162590
Type:
Conference Paper
ISBN:
9781467300407
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.authorRao, Anleien
dc.contributor.authorAlouini, Mohamed-Slimen
dc.date.accessioned2015-08-04T07:01:45Zen
dc.date.available2015-08-04T07:01:45Zen
dc.date.issued2011-12en
dc.identifier.isbn9781467300407en
dc.identifier.doi10.1109/GLOCOMW.2011.6162590en
dc.identifier.urihttp://hdl.handle.net/10754/564467en
dc.description.abstractIn this paper, we introduce a new detector referred to as Geometric mean detector (GEMD) which is based on the ratio of the largest eigenvalue to the Geometric mean of the eigenvalues for collaborative spectrum sensing. The decision threshold has been derived by employing Gaussian approximation approach. In this approach, the two random variables, i.e. The largest eigenvalue and the Geometric mean of the eigenvalues are considered as independent Gaussian random variables such that their cumulative distribution functions (CDFs) are approximated by a univariate Gaussian distribution function for any number of cooperating secondary users and received samples. The approximation approach is based on the calculation of exact analytical moments of the largest eigenvalue and the Geometric mean of the eigenvalues of the received covariance matrix. The decision threshold has been calculated by exploiting the CDF of the ratio of two Gaussian distributed random variables. In this context, we exchange the analytical moments of the two random variables with the moments of the Gaussian distribution function. The performance of the detector is compared with the performance of the energy detector and eigenvalue ratio detector. Analytical and simulation results show that our newly proposed detector yields considerable performance advantage in realistic spectrum sensing scenarios. Moreover, our results based on proposed approximation approach are in perfect agreement with the empirical results. © 2011 IEEE.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.subjectGaussian approximation approachen
dc.subjectGeometric mean detector (GEMD)en
dc.subjectmoments of Geometric mean of eigenvaluesen
dc.subjectmoments of largest eigenvalueen
dc.subjectSpectrum sensingen
dc.titleCollaborative spectrum sensing based on the ratio between largest eigenvalue and Geometric mean of 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.journal2011 IEEE GLOBECOM Workshops (GC Wkshps)en
dc.conference.date5 December 2011 through 9 December 2011en
dc.conference.name2011 IEEE GLOBECOM Workshops, GC Wkshps 2011en
dc.conference.locationHouston, TXen
kaust.authorShakir, Muhammaden
kaust.authorRao, Anleien
kaust.authorAlouini, Mohamed-Slimen
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