Spectrum decision making in distributed cognitive radio networks using an optimal foraging approach

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dc.contributor.advisorAdigun, M.O.
dc.contributor.advisorOlwal, T.O.
dc.contributor.authorOKI, Olukayode Ayodele
dc.date.accessioned2019-09-27T07:53:41Z
dc.date.available2019-09-27T07:53:41Z
dc.date.issued2018
dc.descriptionA Thesis submitted in fulfilment of the requirements for the degree of ‘Doctor of Philosophy’ in the Department of Computer Science Faculty of Science and Agriculture at the University of Zululand, 2018en_US
dc.description.abstractIn the recent past, Cognitive Radio (CR) technology has been regarded in the literature as the most promising technology for performing dynamic spectrum management. One of the major aspects of the spectrum management is referred to as the decision-making ability of CR users. Spectrum decision-making process involves the dynamic spectrum characterisation, selection as well as reconfiguration. The study of dynamic selection and reconfiguration of the frequency and channel bandwidth in spectrum decision-making is important specifically for the realisation of optimal spectrum utilisation in a distributed CR network (CRN). Dynamic spectrum reconfiguration has previously been reported to improve the spectrum utilisation in CRNs. In exploiting spectrum reconfiguration of frequency and channel bandwidth to improve spectrum utilisation, various approaches have been adopted to develop models. However, the existing approaches have not been able to achieve optimal spectrum utilisation due to slow convergence, computational complexity, and repeatability challenges. Hence, the study of the efficacy of dynamic selection and reconfiguration approaches as a mechanism for improving the spectrum utilisation in a distributed CRN remains an open issue. This study addresses this knowledge gap by studying the existing approaches that other researchers have used to develop models for spectrum reconfiguration. This provides unique insight into how those approaches converge slowly, consume computational resources, are non-generic and negatively affect the performance of the models that have been developed from those approaches. The biological approach has generic, simple analytic and high applicability properties. Motivated by the well-established properties of the biological foraging approach, a novel Foraging Inspired Spectrum Selection and Reconfiguration (FISSER) model is proposed. In the proposed FISSER model, foraging animals are considered as Secondary Users (SU), whilst the prey are the available Primary Users’ frequencies. Similar to the biological foraging methodology, whereby the foraging animals search for prey the same way each SU with a message searches for possible available PUs’ frequency to be used for communication. The FISSER model is aimed at achieving both efficient spectrum utilisation and SUs’ node communication in a distributed CRN. The efficacy of the proposed FISSER model has been extensively validated both analytically and through computer simulations. The simulation and analytical results obtained in this study have shown that the FISSER model yields improved communication performance, guarantees the energy efficiency, and maximises the spectrum utilisation compared to the most recently studied approaches.en_US
dc.identifier.urihttps://hdl.handle.net/10530/1841
dc.language.isoenen_US
dc.publisherUniversity of Zululanden_US
dc.subjectCognitive Radio Networksen_US
dc.subjectSpectrum reconfigurationen_US
dc.titleSpectrum decision making in distributed cognitive radio networks using an optimal foraging approachen_US
dc.typeThesisen_US
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