Yau, Kok-Lim Alvin, Komisarczuk, Peter and Teal, Paul D. (2009) Performance analysis of Reinforcement Learning for achieving context-awareness and intelligence in cognitive radio networks. In: IEEE 34th Conference on Local Computer Networks, 2009, 20-23 Oct 2009, Zurich, Switzerland.

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Abstract

Cognitive Radio (CR) is a novel and promising paradigm for next-generation wireless communication. It is able to sense and change its transmission and reception parameters adaptively according to spectrum availability at different channels. The Cognition Cycle (CC) is a state machine that is embodied in each CR that defines the mechanisms related to achieving context-awareness and intelligence including observation, orientation, learning, planning, decision making, and action selection. The CC is the key element in the design of various schemes in CR networks such as Dynamic Channel Selection (DCS), scheduling and congestion control. Hence, a good implementation of the CC is of paramount importance. In this paper, Reinforcement Learning (RL) is employed to implement the CC. The main focus is to analyze the performance of RL as an approach to achieving context-awareness and intelligence in regard to DCS. The contributions of this paper are twofold. Firstly, we seek to justify whether RL is an appropriate tool to implement the CC. Secondly, we seek to understand the effects of changes on RL parameters on network performance. In addition, we propose solutions for the problems associated with the application of RL in DCS. The results presented in this paper show that RL is a promising approach.

Item Type:	Conference or Workshop Item (Paper)
ISSN:	0742-1303
ISBN:	9781424444885
Identifier:	10.1109/LCN.2009.5355028
Page Range:	pp. 1046-1053
Identifier:	10.1109/LCN.2009.5355028
Keywords:	Performance analysis, Intelligent networks, Cognitive radio, Chromium, Distributed control, Context, Learning systems, Wireless communication, Availability, Cognition
Subjects:	Computing
Depositing User:	Vani Aul
Date Deposited:	21 Mar 2014 14:56
Last Modified:	28 Aug 2021 07:05
URI:	https://repository.uwl.ac.uk/id/eprint/786

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