Research Article
Collision reduction in cognitive radio using multichannel 1-persistent CSMA combined with reinforcement learning
@INPROCEEDINGS{10.4108/ICST.CROWNCOM2010.9294, author={Haibin Li and David Grace and Paul D. Mitchell}, title={Collision reduction in cognitive radio using multichannel 1-persistent CSMA combined with reinforcement learning}, proceedings={5th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications}, publisher={IEEE}, proceedings_a={CROWNCOM}, year={2010}, month={9}, keywords={Channel Assignment Cognitive Radio Multichannel CSMA Multiple Access Schemes Reinforcement Learning}, doi={10.4108/ICST.CROWNCOM2010.9294} }- Haibin Li
David Grace
Paul D. Mitchell
Year: 2010
Collision reduction in cognitive radio using multichannel 1-persistent CSMA combined with reinforcement learning
CROWNCOM
IEEE
DOI: 10.4108/ICST.CROWNCOM2010.9294
Abstract
In this paper a novel multiple access scheme, M-CSMA-RL, is proposed for secondary users which combines multichannel 1-persistent CSMA and reinforcement learning. The scheme effectively reduces the probability of packet collisions among primary and secondary users sharing common spectrum. Compared with multichannel CSMA without learning, the throughput and packet loss of M-CSMA-RL shows a significant improvement in a distributed cognitive radio scenario in situations where primary users operate with TDMA/FDMA. The results show how the M-CSMA-RL scheme improves both primary and secondary user's throughput at various offered traffic levels and with different ratios of primary and secondary user offered traffic.
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