Dynamic channel selection with reinforcement learning for cognitive WLAN over fiber

Authors:
Yi Li;Hong Ji;Xi Li;Victor C.M. Leung
Affiliations:
Key Laboratory of Universal Wireless Communications, Ministry of Education Beijing University of Posts and Telecommunications, Beijing, China;Key Laboratory of Universal Wireless Communications, Ministry of Education Beijing University of Posts and Telecommunications, Beijing, China;Key Laboratory of Universal Wireless Communications, Ministry of Education Beijing University of Posts and Telecommunications, Beijing, China;Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, Canada
Venue:
International Journal of Communication Systems
Year:
2012

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Abstract

The Internet of Things (IoT) is the next big possibility and challenge for the future information networks. It makes the interaction between people and things more active and provides the connection among different existing networks. Ubiquitous short-range wireless access and cognitive radio are key technologies for the IoT's realization. This paper deals with some problems in an integrated system of wireless local area network (WLAN) and cognitive radio — cognitive WLAN over fiber (CWLANoF). CWLANoF is a cost-effective and efficient architecture that combines radio over fiber and cognitive radio technologies to provide centralized radio resource management and equal spectrum access in infrastructure-based IEEE 802.11 WLANs. In this paper, a reinforcement learning approach is applied to implement dynamic channel selection in CWLANoF. The cognitive access points select the best channels among the industrial, scientific, and medical band for data packet transmission, given that the objective is to minimize external interference and acquire better network-wide performance. The reinforcement learning method avoids solving complex optimization problems while being able to explore the states of a CWLANoF system during normal operations. Simulation results reveal that the proposed strategy is effective in avoiding aggregated interference, reducing outage probability, and improving network throughput. Copyright © 2012 John Wiley & Sons, Ltd.