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http://dx.doi.org/10.6109/jkiice.2022.26.7.1026

Reinforcement Learning based Multi-Channel MAC Protocol for Cognitive Radio Ad-hoc Networks  

Park, Hyung-Kun (School of Electrical Electronic and Communication Engineering, KOREATECH)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.7, 2022 , pp. 1026-1031 More about this Journal
Abstract
Cognitive Radio Ad-Hoc Networks (CRAHNs) enable to overcome the shortage of frequency resources due to the increase of radio services. In order to avoid interference with the primary user in CRANH, channel sensing to check the idle channel is required, and when the primary user appears, the time delay due to handover should be minimized through fast idle channel selection. In this paper, throughput was improved by reducing the number of channel sensing and preferentially sensing a channel with a high probability of being idle, using reinforcement learning. In addition, we proposed a multi-channel MAC (Medium Access Control) protocol that can minimize the possibility of collision with the primary user by sensing the channel at the time of data transmission without performing periodic sensing. The performance was compared and analyzed through computer simulation.
Keywords
cognitive radio ad-hoc networks; medium access control; channel sensing; reinforcement learning; Q-learning;
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