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Multi‐agent spectrum access with sensing skipping based on reinforcement learning
Author(s) -
Zeng Linghui,
Zhang Jianzhao
Publication year - 2021
Publication title -
transactions on emerging telecommunications technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.366
H-Index - 47
ISSN - 2161-3915
DOI - 10.1002/ett.4264
Subject(s) - reinforcement learning , computer science , overhead (engineering) , channel (broadcasting) , idle , spectrum (functional analysis) , throughput , selection (genetic algorithm) , scheme (mathematics) , real time computing , distributed computing , computer network , wireless , telecommunications , artificial intelligence , mathematics , mathematical analysis , physics , quantum mechanics , operating system
The spectrum sharing among high‐density users in the dynamic spectrum environment of future mobile communication systems is analyzed in this article. First, the channel competitions of multiple users are modeled by multi‐player multi‐armed bandit. Then, a channel selection strategy based on the distributed reinforcement learning is proposed, with which the channel selection could be optimized online in accordance to the real‐time competition condition. Thus the collisions of channel access can be effectively reduced with relatively less cooperation overhead. Furthermore, a Bayesian estimation‐based spectrum sensing skipping scheme is designed, which avoids unnecessary spectrum sensing through predicting the idle time of channels. Simulation results demonstrate that the the spectrum sensing times can be reduced by at least 37% and the system throughput can be improved by 1.5 times compared to existing solutions.