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dc.contributor.authorShakir, Muhammad
dc.contributor.authorAlouini, Mohamed-Slim
dc.date.accessioned2015-08-03T09:35:30Z
dc.date.available2015-08-03T09:35:30Z
dc.date.issued2012-04-28
dc.identifier.citationShakir, M. Z., & Alouini, M.-S. (2012). Generalized Eigenvalue Based Spectrum Sensing. Cognitive Radio and Its Application for Next Generation Cellular and Wireless Networks, 139–176. doi:10.1007/978-94-007-1827-2_6
dc.identifier.isbn9789400718265
dc.identifier.issn18761100
dc.identifier.doi10.1007/978-94-007-1827-2_6
dc.identifier.urihttp://hdl.handle.net/10754/561979
dc.description.abstractSpectrum sensing is one of the fundamental components in cognitive radio networks. In this chapter, a generalized spectrum sensing framework which is referred to as Generalized Mean Detector (GMD) has been introduced. In this context, we generalize the detectors based on the eigenvalues of the received signal covariance matrix and transform the eigenvalue based spectrum sensing detectors namely: (i) the Eigenvalue Ratio Detector (ERD) and two newly proposed detectors which are referred to as (ii) the GEometric Mean Detector (GEMD) and (iii) the ARithmetic Mean Detector (ARMD) into an unified framework of generalize spectrum sensing. The foundation of the proposed framework is based on the calculation of exact analytical moments of the random variables of the decision threshold of the respective detectors. The decision threshold has been calculated in a closed form which is based on the approximation of Cumulative Distribution Functions (CDFs) of the respective test statistics. In this context, we exchange the analytical moments of the two random variables of the respective test statistics with the moments of the Gaussian (or Gamma) distribution function. The performance of the eigenvalue based detectors is compared with the several traditional detectors including the energy detector (ED) to validate the importance of the eigenvalue based detectors and the performance of the GEMD and the ARMD particularly in realistic wireless cognitive radio network. Analytical and simulation results show that the newly proposed detectors yields considerable performance advantage in realistic spectrum sensing scenarios. Moreover, the presented results based on proposed approximation approaches are in perfect agreement with the empirical results. © 2012 Springer Science+Business Media Dordrecht.
dc.publisherSpringer Nature
dc.titleGeneralized eigenvalue based spectrum sensing
dc.typeArticle
dc.contributor.departmentCommunication Theory Lab
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalCognitive Radio and its Application for Next Generation Cellular and Wireless Networks
kaust.personShakir, Muhammad
kaust.personAlouini, Mohamed-Slim
dc.date.published-online2012-04-28
dc.date.published-print2012


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