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

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publication2023 IEEE Wireless Communications and Networking Conference, WCNC 2023 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665491228
DOIs
StatePublished - 2023
Event2023 IEEE Wireless Communications and Networking Conference, WCNC 2023 - Glasgow, United Kingdom
Duration: 26 Mar 202329 Mar 2023

Publication series

NameIEEE Wireless Communications and Networking Conference, WCNC
Volume2023-March
ISSN (Print)1525-3511

Conference

Conference2023 IEEE Wireless Communications and Networking Conference, WCNC 2023
Country/TerritoryUnited Kingdom
CityGlasgow
Period26/03/2329/03/23

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