Simple Reinforcement Learning based Contention Windows Adjustment for IEEE 802.11 Networks
2023 IEEE 20TH CONSUMER COMMUNICATIONS & NETWORKING CONFERENCE, CCNC(2023)
摘要
This paper proposes a simple reinforcement learning-based CW adjustment for IEEE 802.11 Networks. In the proposed scheme, each node finds an optimal value of CWmin for networks from its transmission attempts. The proposed scheme applies a multi-armed bandit solution which is the most simple way among general reinforcement learning methods. In addition, we introduce a novel rewarding policy for the proposed scheme. This contributes to a simple and light implementation for even poor wireless devices. We demonstrate the proposed method provides almost the same network performance as the conventional method through the computer simulation results.
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关键词
IEEE 802.11,Reinforcement-learning,Multiarmed bandit (MAB),Distribute coordination function (DCF),Q-learning
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