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dc.contributor.advisorGezici, Sinan
dc.contributor.authorKoçak, Fatih
dc.date.accessioned2016-01-08T18:13:44Z
dc.date.available2016-01-08T18:13:44Z
dc.date.issued2010
dc.identifier.urihttp://hdl.handle.net/11693/15118
dc.descriptionAnkara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent University, 2010.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2010.en_US
dc.descriptionIncludes bibliographical references leaves 87-95.en_US
dc.description.abstractIn this thesis, the time-delay estimation problem is studied for cognitive radio systems, multiple-input single-output (MISO) systems, and cognitive single-input multiple-output (SIMO) systems. A two-step approach is proposed for cognitive radio and cognitive SIMO systems in order to perform time-delay estimation with significantly lower computational complexity than the optimal maximum likelihood (ML) estimator. In the first step of this two-step approach, an ML estimator is used for each receiver branch in order to estimate the unknown parameters of the signal received via that branch. Then, in the second step, the estimates from the first step are combined in various ways in order to obtain the final time-delay estimate. The combining techniques that are used in the second step are called optimal combining, signal-to-noise ratio (SNR) combining, selection combining, and equal combining. It is shown that the performance of the optimal combining technique gets very close to the Cramer-Rao lower bound (CRLB) at high SNRs. These combining techniques provide various mechanisms for diversity combining for time-delay estimation and extend the concept of diversity in communications systems to the time-delay estimation problem in cognitive radio and cognitive SIMO systems. Simulation results are presented to evaluate the performance of the proposed estimators and to verify the theoretical analysis. For the solution of the time-delay estimation problem in MISO systems, ML estimation based on a genetic global optimization algorithm, namely, differential evolution (DE), is proposed. This approach is proposed in order to decrease the computational complexity of the ML estimator, which results in a complex optimization problem in general. A theoretical analysis is carried out by deriving the CRLB. Simulation studies for Rayleigh and Rician fading scenarios are performed to investigate the performance of the proposed algorithm.en_US
dc.description.statementofresponsibilityKoçak, Fatihen_US
dc.format.extentxiii, 95 leaves, illustrationsen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectTime-delay estimationen_US
dc.subjectCramer-Rao lower bound (CRLB)en_US
dc.subjectMaximum likelihood (ML) estimatoren_US
dc.subjectCognitive single-input multiple-output (SIMO) systemsen_US
dc.subjectMultiple-input single-output (MISO) systemsen_US
dc.subjectCognitive radioen_US
dc.subject.lccTK5103.2 .K63 2010en_US
dc.subject.lcshMIMO systems.en_US
dc.subject.lcshTime delay systems.en_US
dc.subject.lcshSoftware radio.en_US
dc.subject.lcshWireless communication systems.en_US
dc.subject.lcshSpace time codes.en_US
dc.titleTime-delay estimation in cognitive radio and MIMO systemsen_US
dc.typeThesisen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidB122166


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