Learning-based autonomous channel access in the presence of hidden terminals
File(s)
Accepted version
Author(s)
Type
Journal Article
Abstract
We consider the problem of autonomous channel access (AutoCA), where a group of terminals tries to discover a communication strategy with an access point (AP) via a common wireless channel in a distributed fashion. Due to the irregular topology and the limited communication range of terminals, a practical challenge for AutoCA is the hidden terminal problem, which is notorious in wireless networks for deteriorating throughput and delay performances. To meet the challenge, this paper presents a new multi-agent deep reinforcement learning paradigm, dubbed MADRL-HT, tailored for AutoCA in the presence of hidden terminals. MADRL-HT exploits topological insights and transforms the observation space of each terminal into a scalable form independent of the number of terminals. To compensate for the partial observability, we put forth a look-back mechanism such that the terminals can infer behaviors of their hidden terminals from the carrier-sensed channel states as well as feedback from the AP. A window-based global reward function is proposed, whereby the terminals are instructed to maximize the system throughput while balancing the terminals’ transmission opportunities over the course of learning. Considering short-packet machine-type communications, extensive numerical experiments verified the superior performance of our solution benchmarked against the legacy carrier-sense multiple access with collision avoidance (CSMA/CA) protocol.
Date Issued
2024-05-01
Date Acceptance
2023-05-18
Citation
IEEE Transactions on Mobile Computing, 2024, 23 (5), pp.3680-3695
ISSN
1536-1233
Publisher
Institute of Electrical and Electronics Engineers
Start Page
3680
End Page
3695
Journal / Book Title
IEEE Transactions on Mobile Computing
Volume
23
Issue
5
Copyright Statement
Copyright © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subjects
Computer Science
Computer Science, Information Systems
Delays
Hidden terminal
IEEE 802.11 Standard
multi-agent deep reinforcement learning
multiple channel access
proximal policy optimization
Receivers
Reinforcement learning
Science & Technology
Sensors
Technology
Telecommunications
Throughput
Wi-Fi
Wireless fidelity
Publication Status
Published
Date Publish Online
2023-06-05