Applications of reinforcement learning to Cognitive Radio Networks

Yau, K.-L., Komisarczuk, Peter and Teal, P. D. (2010) Applications of reinforcement learning to Cognitive Radio Networks. In: 1st International Workshop on Cognitive Radio Interfaces and Signal Processing (CRISP 2010), 23-27 May 2010, Cape Town, South Africa.

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Cognitive Radio (CR) enables an unlicensed user
to change its transmission and reception parameters adaptively
according to spectrum availability in a wide range of licensed
channels. The concept of a Cognition Cycle (CC) is the key
element of CR to provide context awareness and intelligence so
that each unlicensed user is able to observe and carry out an
optimal action on its operating environment for performance
enhancement. The CC can be applied in various application
schemes in CR networks such as Dynamic Channel Selection
(DCS), topology management, congestion control, and
scheduling. In this paper, Reinforcement Learning (RL) is
applied to implement the conceptual of the CC. We provide an
extensive overview of our work including single-agent and
multi-agent approaches to show that RL is a promising
technique. Our contribution in this paper is to propose various
application schemes using our RL approach to warrant further
research on RL in CR networks.

Item Type: Conference or Workshop Item (Paper)
Subjects: Computer science, knowledge and information systems
Depositing User: Vani Aul
Date Deposited: 21 Mar 2014 14:53
Last Modified: 11 Dec 2015 11:09

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