Citation

Al-Shami, Tareq Mohammed Ali (2016) Multiobjective Optimisation Of Joint Power And Admission Control In Cognitive Radio Networks Using Enhanced Swarm Intelligence. Masters thesis, Multimedia University.

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Abstract

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.

Item Type:	Thesis (Masters)
Additional Information:	Call No.: Q337.3 .T37 2016
Uncontrolled Keywords:	Swarm intelligence
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	21 May 2018 15:47
Last Modified:	13 Aug 2018 14:17
URII:	http://shdl.mmu.edu.my/id/eprint/7151

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