Study of Contention Window Adjustment for CSMA/CA by Using Machine Learning

Yen Wen Chen, Kuo Che Kao

研究成果: 書貢獻/報告類型會議論文篇章同行評審

摘要

In IEEE 802.11, CSMA/CA protocol applies the exponential backoff scheme to relax the contention problems among different clients wishing to transmit data at the same time. A client shall randomly choose a number of time slots bounded by the contention window. As the size of initial contention window is fixed for each device without considering the congestion status of the network, it may worsen the congestion condition for smaller contention window size, or may waste radio resource for larger window size in traditional scheme. In this paper, we propose the reinforcement learning model rewarded by throughput to dynamically adjust the contention window periodically. The Q-learning model is utilized in the proposed scheme and the reward function is determined by the current throughput measured during a certain interval and which measured in previous interval. The simulation results were compared with the rule based approach used in current 802.11 networks. The results show that the proposed scheme can effectively decrease the collision rate and the system throughput increases significantly accordingly. We also change the number of clients during the simulation time to examine the adjustment capability of the proposed scheme. The results also show that the proposed scheme is still superior to the rule based scheme in such environment.

原文???core.languages.en_GB???
主出版物標題2021 22nd Asia-Pacific Network Operations and Management Symposium, APNOMS 2021
發行者Institute of Electrical and Electronics Engineers Inc.
頁面206-209
頁數4
ISBN(電子)9784885523328
DOIs
出版狀態已出版 - 8 9月 2021
事件22nd Asia-Pacific Network Operations and Management Symposium, APNOMS 2021 - Virtual, Online, Taiwan
持續時間: 8 9月 202110 9月 2021

出版系列

名字2021 22nd Asia-Pacific Network Operations and Management Symposium, APNOMS 2021

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???event.eventtypes.event.conference???22nd Asia-Pacific Network Operations and Management Symposium, APNOMS 2021
國家/地區Taiwan
城市Virtual, Online
期間8/09/2110/09/21

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