A Comparative Analysis of Optimization Techniques in Cognitive Radio (QoS)
Sandeep P, Department of Electronics & Communication Engineering, Sri Satya Sai University of Technology and Medical Sciences, Sehore, Bhopal (M.P). India.
Manuscript received on 15 February 2017 | Revised Manuscript received on 22 February 2017 | Manuscript Published on 28 February 2017 | PP: 97-102 | Volume-6 Issue-3, February 2017 | Retrieval Number: C4845026317/17©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Wireless Technology has seen a tremendous advancement in recent times. There has been a huge growth in multimedia applications over the wireless networks. The requirement of significant bandwidth for multimedia services has increased the demand for radio spectrum. The scarcity of radio spectrum has become a challenge for the conventional fixed spectrum assignment policy. Thus, Cognitive Radio (CR) has emerged as a new exclusive choice to address the spectrum underutilization problem by enabling users to opportunistically access unused spectrum bands. It offers a promising solution to meet this demand by fully utilizing available spectrum resources. It improves the utilization of the wireless spectrum by allowing the secondary users to access the primary channels in an opportunistic manner. Efficient utilization of frequency spectrum is possible using dynamic spectrum allocation. Optimization techniques like Genetic Algorithm(GA), Ant Colony Optimization (ACO) and Mutated Ant Colony Optimization (MACO) are discussed here to meet the users QoS needs in the Cognitive Radio. The transmission and environmental parameters along with performance objectives of cognitive radio are studied and compared in the paper using different optimization techniques. In this paper, the results of various optimization techniques in Cognitive Radio System along with CR objectives are analysed to meet users QoS.
Keywords: Cognitive Radio Genetic Algorithm, Ant Colony Optimization, Mutated Ant Colony Optimization, QoS Provisioning.
Scope of the Article: Cross-Layer Optimization