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Title:	Multi-objective evolutionary algorithms for cooperative spectrum sensing in cognitive radio networks
Authors:	Ayman Abd El-Saleh (P38294)
Supervisor:	Mahamod Ismail, Prof. Dr.
Keywords:	Cognitive radio networks Genetic algorithms Evolutionary computation Universiti Kebangsaan Malaysia -- Dissertations Dissertations, Academic -- Malaysia
Issue Date:	25-Jun-2012
Description:	Cognitive radio (CR) users are envisioned as intelligent secondary users (SUs) armed with spectrum sensing capabilities to monitor licensees' or primary users' (PUs') activities and opportunistically access unused channels. In cognitive radio networks (CRNs), cooperative spectrum sensing is performed with an essential aim of maximizing CRN throughput and PU protection simultaneously. However, these two objectives are contradictory because improving the PU protection, through cooperation between multiple SUs, results in an increased relaying overhead. The main objective of this research is to develop multi-objective evolutionary algorithms (MOEAs) based on genetic algorithms (GAs) for optimizing the performance of CRNs through attaining compromises between conflicting objectives according to predefined criteria. In this thesis, the tradeoff between the conflicting objectives of existing GA-based CR systems is first studied. Then, three different CRN architectures have been proposed; namely, hard decision fusion (HDF), soft data fusion (SDF) and hybrid SDF-HDF cluster-based CRNs. The proposed architectures satisfy distinct requirements of PU detection performance and overhead traffic. The SDF-based architecture can be used when high PU detection performance is needed whereas the HDF-based one is to be used when low overhead traffic is demanding. In contrast, the hybrid SDF-HDF cluster-based architecture is used when a balanced compromise between PU detection performance and overhead traffic is of interest. In this research, MOEAs based on GAs called single-objective GA (SOGA), bi-objective GA (BOGA), and multi-objective GA (MOGA) have been developed as intelligent optimization systems for the proposed SDF, HDF and hybrid SDF-HDF cluster-based CRN architectures, respectively. The network deployments of the different CRN architectures are initially proposed and their mathematical formalisms are derived, simulated, and analyzed to observe existing tradeoffs between their respective objectives. Based on pre-simulation results, objective functions and their corresponding dependency relationships with design parameters for each proposed network have been then identified and formulated. These conflicting objectives are introduced as performance metrics for the corresponding MOEA. The performance of the MOEA-based optimization systems of CRN architectures are evaluated under different channel conditions and operational scenarios. Post-analyses and comparisons are finally carried out to verify the conceptual functionality and validate the obtained results. Simulation results of MOEAs of CRNs show successful adaption in response to distinct environmental and topological conditions. In SDF-based CRNs, the proposed SOGA achieves a fitness score of 10% higher than that of the best conventional SDF scheme given the same false alarm rate. The BOGA optimization system of HDF-based CRN achieves a fitness score improvement of 10% and 4% than when using static cooperation level and static sensing time, respectively. In hybrid SDF-HDF cluster-based CRN, the proposed MOGA optimization system, when using its optimal parameters, show a fitness score improvement of about 14.4% greater than that if the parameters are set to their maximum values and an improvement of about 69.3% when they are set to their minimum values.,"Certification of Master's/Doctoral Thesis" is not available,Ph.D.
Pages:	198
Call Number:	TK5103.4815S238 2012 3 tesis
Publisher:	UKM, Bangi
Appears in Collections:	Faculty of Engineering and Built Environment / Fakulti Kejuruteraan dan Alam Bina

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