Learning mechanisms for achieving context awareness and intelligence in Cognitive Radio Networks

Yau, K.-L., Komisarczuk, Peter and Teal, P. D. (2011) Learning mechanisms for achieving context awareness and intelligence in Cognitive Radio Networks. In: 36th Conference on Local Computer Networks (LCN), 2011 IEEE, 4-7 October 2011, Bonn, Germany.

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Providing that licensed or Primary Users (PUs) are
oblivious to the presence of unlicensed or Secondary Users (SUs),
Cognitive Radio (CR) enables the SUs to use underutilized
licensed spectrum (or white spaces) opportunistically and
temporarily conditional on the interference to the PUs being
below an acceptable level. Context awareness and intelligence
enable the SU to sense for and use the underutilized licensed
spectrum in an efficient manner. This paper investigates various
learning mechanisms for achieving context awareness and
intelligence with respect to Dynamic Channel Selection (DCS) in
CR networks. The learning mechanisms are Adaptation (Adapt),
Window (Win), Adaptation-Window (AdaptWin), and
Reinforcement Learning (RL). The DCS scheme helps SU base
station to select channel adaptively for data transmission to its
SU host in static and mobile centralized CR networks. The
purpose is to enhance quality of service, particularly throughput
and delay (in terms of number of channel switches), in the
presence of channel heterogeneity. Our contribution is to
investigate simple and yet pragmatic learning mechanisms for
CR networks. Simulation results reveal that RL, AdaptWin and
Win achieve approximately similar and the best possible network
performance, followed by Adapt, and finally Random, which
does not apply learning and serves as baseline.

Item Type: Conference or Workshop Item (Paper)
Subjects: Computing
Depositing User: Vani Aul
Date Deposited: 21 Mar 2014 15:12
Last Modified: 04 Dec 2015 14:08
URI: http://repository.uwl.ac.uk/id/eprint/772

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