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dc.contributor.advisorWalingo, Tom Mmbasu.
dc.creatorKataka, Matsanza Edwin.
dc.date.accessioned2018-10-17T09:39:42Z
dc.date.available2018-10-17T09:39:42Z
dc.date.created2017
dc.date.issued2017
dc.identifier.urihttp://hdl.handle.net/10413/15684
dc.descriptionMaster of Science in Electronic Engineering. University of KwaZulu-Natal, Durban 2017.en_US
dc.description.abstractThe demand for spectrum is increasing particularly due to the accelerating growth in wireless data traffic generated by smart phones, tablets and other internet access devices. Most of prime spectrum is already licensed. The licensed spectrum is underutilized or used inefficiently, i.e. spectrum sits idle at any given time and location. Opportunistic Spectrum Access (OSA) is proposed as a solution to provide access to the temporarily unused spectrum commonly known as white spaces to improve spectrum utilization, increase spectrum efficiency and reduce spectrum scarcity. The aim of this research is to investigate potential impact of cooperative spectrum sensing techniques technologies on spectrum management. To fulfill this we focused on two spectrum sensing techniques namely; Firstly energy efficient statistical cooperative spectrum sensing in cognitive radio networks, this work exploits the higher order statistical (HOS) tests to detect the status of PU signal by a group of SUs. Secondly, an optimal energy based cooperative spectrum sensing in cognitive radio networks was investigated. In this work the performance of optimal hard fusion rules are employed in SU’s selection criteria and fusion of the decisions under Gaussian channel and Rayleigh channels. To optimize on the energy a two stage fusion and selection strategy is adopted to minimize the number of collaborating SUs.en_US
dc.language.isoen_ZAen_US
dc.subjectTheses - Electronic Engineering.en_US
dc.subject.otherSpectrum sensing.en_US
dc.subject.otherCognitive radio.en_US
dc.subject.otherEnergy detection schemes.en_US
dc.subject.otherCooperative sensing.en_US
dc.titleEnergy efficient cooperative spectrum sensing techniques in cognitive radio networks.en_US
dc.typeThesisen_US


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