Long-/Short-Term Reinforcement Learning for Multi-APs Channel Allocation in IEEE 802.11ax WLANs

Sheng Han Chung*, Li Hsiang Shen, Kai Ten Feng

*此作品的通信作者

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

IEEE 802.11ax system has been adopted to provide enhanced throughput performance for next-generation wireless local area networks. Its orthogonal frequency division multiple access (OFDMA) allows massive users to concurrently utilize different subbands for data transmission from their corresponding access points (APs). However, severe adjacent channel interference (ACI) incurs overlapping channels under the scenarios of dense users with multiple APs, which should be properly alleviated to provide adequate system throughput. In this paper, we propose a long-/short-term reinforcement learning channel allocation (LSRCA) scheme to effectively mitigate ACI for multi-AP scenarios in IEEE 802.11ax systems. With the considerations of signal features from both long and short time durations, the LSRCA algorithm can maximize effective sum rate through online adaptation and learning via the updates of two Q-tables for weighting adjustments and action execution. Experimental results in realistic fields have demonstrated the effectiveness of LSRCA scheme by providing higher system throughput compared to existing benchmark methods.

原文English
主出版物標題2023 IEEE Wireless Communications and Networking Conference, WCNC 2023 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781665491228
DOIs
出版狀態Published - 2023
事件2023 IEEE Wireless Communications and Networking Conference, WCNC 2023 - Glasgow, United Kingdom
持續時間: 26 3月 202329 3月 2023

出版系列

名字IEEE Wireless Communications and Networking Conference, WCNC
2023-March
ISSN(列印)1525-3511

Conference

Conference2023 IEEE Wireless Communications and Networking Conference, WCNC 2023
國家/地區United Kingdom
城市Glasgow
期間26/03/2329/03/23

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