Multiobjective Optimisation Of Joint Power And Admission Control In Cognitive Radio Networks Using Enhanced Swarm Intelligence

Cognitive radio (CR) is a promising wireless technology that aims to utilize the available spectrum intelligently. Underlay cognitive radio networks (CRNs) is a potential vision of future CR systems where the unlicensed users or secondary users (SUs) can peacefully share the spectrum with the licens...

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Main Author:	Al-Shami, Tareq Mohammed Ali
Format:	Thesis
Published:	2016
Subjects:	Q300-390 Cybernetics
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id	my-mmu-ep.7151
record_format	uketd_dc
spelling	my-mmu-ep.71512018-08-13T14:17:36Z Multiobjective Optimisation Of Joint Power And Admission Control In Cognitive Radio Networks Using Enhanced Swarm Intelligence 2016-06 Al-Shami, Tareq Mohammed Ali Q300-390 Cybernetics Cognitive radio (CR) is a promising wireless technology that aims to utilize the available spectrum intelligently. Underlay cognitive radio networks (CRNs) is a potential vision of future CR systems where the unlicensed users or secondary users (SUs) can peacefully share the spectrum with the licensed users or primary users (PUs) without causing harmful interference to the primary network. The problem of joint power and admission control (JPAC) is a critical issue encountered in underlay CRNs. Several research works have attempted to address the aforementioned problem. However, the main focus of optimizing the JPAC problem was either to maximize the system capacity or minimize the power consumption, not both. Moving forward towards 5G realization where optimisation is envisioned to take place in multiple performance dimensions, it is crucially desirable to achieve high data rate with low power consumption. In this work, a multiobjective JPAC optimisation problem that jointly maximizes the sum throughput and minimizes power consumption in underlay CRNs is formulated. Particle swarm optimisation (PSO) is an attractive evolutionary algorithm that is widely used in optimizing many real-world engineering problems. However, the standard PSO (SPSO) suffers from premature convergence which occurs due to the unbalance between exploration and exploitation. In this research, PSO has been used as the optimizing core of the aforementioned multiobjective JPAC problem. To improve the performance of PSO, two novel continuous and binary PSO variants have been developed. 2016-06 Thesis http://shdl.mmu.edu.my/7151/ http://library.mmu.edu.my/diglib/onlinedb/dig_lib.php masters Multimedia University Faculty of Engineering
institution	Multimedia University
collection	MMU Institutional Repository
topic	Q300-390 Cybernetics
spellingShingle	Q300-390 Cybernetics Al-Shami, Tareq Mohammed Ali Multiobjective Optimisation Of Joint Power And Admission Control In Cognitive Radio Networks Using Enhanced Swarm Intelligence
description	Cognitive radio (CR) is a promising wireless technology that aims to utilize the available spectrum intelligently. Underlay cognitive radio networks (CRNs) is a potential vision of future CR systems where the unlicensed users or secondary users (SUs) can peacefully share the spectrum with the licensed users or primary users (PUs) without causing harmful interference to the primary network. The problem of joint power and admission control (JPAC) is a critical issue encountered in underlay CRNs. Several research works have attempted to address the aforementioned problem. However, the main focus of optimizing the JPAC problem was either to maximize the system capacity or minimize the power consumption, not both. Moving forward towards 5G realization where optimisation is envisioned to take place in multiple performance dimensions, it is crucially desirable to achieve high data rate with low power consumption. In this work, a multiobjective JPAC optimisation problem that jointly maximizes the sum throughput and minimizes power consumption in underlay CRNs is formulated. Particle swarm optimisation (PSO) is an attractive evolutionary algorithm that is widely used in optimizing many real-world engineering problems. However, the standard PSO (SPSO) suffers from premature convergence which occurs due to the unbalance between exploration and exploitation. In this research, PSO has been used as the optimizing core of the aforementioned multiobjective JPAC problem. To improve the performance of PSO, two novel continuous and binary PSO variants have been developed.
format	Thesis
qualification_level	Master's degree
author	Al-Shami, Tareq Mohammed Ali
author_facet	Al-Shami, Tareq Mohammed Ali
author_sort	Al-Shami, Tareq Mohammed Ali
title	Multiobjective Optimisation Of Joint Power And Admission Control In Cognitive Radio Networks Using Enhanced Swarm Intelligence
title_short	Multiobjective Optimisation Of Joint Power And Admission Control In Cognitive Radio Networks Using Enhanced Swarm Intelligence
title_full	Multiobjective Optimisation Of Joint Power And Admission Control In Cognitive Radio Networks Using Enhanced Swarm Intelligence
title_fullStr	Multiobjective Optimisation Of Joint Power And Admission Control In Cognitive Radio Networks Using Enhanced Swarm Intelligence
title_full_unstemmed	Multiobjective Optimisation Of Joint Power And Admission Control In Cognitive Radio Networks Using Enhanced Swarm Intelligence
title_sort	multiobjective optimisation of joint power and admission control in cognitive radio networks using enhanced swarm intelligence
granting_institution	Multimedia University
granting_department	Faculty of Engineering
publishDate	2016
_version_	1747829654317694976

Multiobjective Optimisation Of Joint Power And Admission Control In Cognitive Radio Networks Using Enhanced Swarm Intelligence

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