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dc.contributor.advisorGezici, Sinan
dc.contributor.authorTurgut, Esma
dc.date.accessioned2016-01-08T18:19:47Z
dc.date.available2016-01-08T18:19:47Z
dc.date.issued2012
dc.identifier.urihttp://hdl.handle.net/11693/15523
dc.descriptionAnkara : The Department of Electrical and Electronics Engineering and the Graduate School of Engineering and Science of Bilkent University, 2012.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2012.en_US
dc.descriptionIncludes bibliographical refences.en_US
dc.description.abstractOver the past decade, the demand for wireless technologies has increased enormously, which leads to a perceived scarcity of the frequency spectrum. Meanwhile, static allocation of the frequency spectrum leads to under-utilization of the spectral resources. Therefore, dynamic spectrum access has become a necessity. Cognitive radio has emerged as a key technology to solve the conflicts between spectrum scarcity and spectrum under-utilization. It is an intelligent wireless communication system that is aware of its operating environment and can adjust its parameters in order to allow unlicensed (secondary) users to access and communicate over the frequency bands assigned to licensed (primary) users when they are inactive. Therefore, cognitive radio requires reliable spectrum sensing techniques in order to avoid interference to primary users. In this thesis, the spectrum sensing problem in cognitive radio is studied. Specifically, the restricted Neyman-Pearson (NP) approach, which maximizes the average detection probability under the constraints on the minimum detection and false alarm probabilities, is applied to the spectrum sensing problem in cognitive radio systems in the presence of uncertainty in the prior probability distribution of primary users’ signals. First, we study this problem in the presence of Gaussian noise and assume that primary users’ signals are Gaussian. Then, the problem is reconsidered for non-Gaussian noise channels. Simulation results are obtained in order to compare the performance of the restricted NP approach with the existing methods such as the generalized likelihood ratio test (GLRT) and energy detection. The restricted NP approach outperforms energy detection in all cases. It is also shown that the restricted NP approach can provide important advantages over the GLRT in terms of the worst-case detection probability, and sometimes in terms of the average detection probability depending on the situation in the presence of imperfect prior information for Gaussian mixture noise channels.en_US
dc.description.statementofresponsibilityTurgut, Esmaen_US
dc.format.extentxi, 53 leaves, illustrationsen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCognitive radioen_US
dc.subjectdetectionen_US
dc.subjectspectrum sensingen_US
dc.subjectNeyman-Pearsonen_US
dc.subject.lccTK5103.4815 .T87 2012en_US
dc.subject.lcshCognitive radio networks.en_US
dc.subject.lcshSoftware radio.en_US
dc.subject.lcshWireless communication systems.en_US
dc.titleRestricted Neyman-Pearson approach based spectrum sensing in cognitive radio systemsen_US
dc.typeThesisen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US


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