Non-Uniform Time-Step Deep Q-Network for Carrier-Sense Multiple Access in Heterogeneous Wireless Networks
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AbstractThis paper investigates CSMA protocols that employ deep reinforcement learning (DRL) techniques, referred to as carrier-sense deep-reinforcement learning multiple access (CS-DLMA). The goal of CS-DLMA is to enable efficient spectrum sharing among a group of co-located heterogeneous wireless networks. Existing CSMA protocols, such as the MAC of WiFi, are designed for a homogeneous network. Such protocols suffer from severe performance degradation in heterogeneous environments. CS-DLMA aims to circumvent this problem by making use of DRL. In particular, this paper adopts alpha-fairness as the objective of CS-DLMA. With alpha-fairness, CS-DLMA can achieve different objectives when coexisting with other MACs. A salient feature of CS-DLMA is that it can achieve these objectives without knowing the coexisting MACs. The underpinning DRL technique in CS-DLMA is deep Q-network (DQN). However, the conventional DQN algorithms are not suitable for CS-DLMA due to uniform time-step assumption. In CSMA protocols, time steps are non-uniform in that the time durations for carrier sensing and data transmission are different. This paper introduces non-uniform time-step DQN to address this issue. Our simulation results show that CS-DLMA can achieve alpha-fairness objective when coexisting with TDMA, ALOHA, and WiFi protocols. We also find that CS-DLMA is more Pareto efficient than other CSMA protocols when coexisting with WiFi.
Acceptance Date27/04/2020
All Author(s) ListY. Yu, S. C. Liew, T. Wang
Journal nameIEEE Transactions on Mobile Computing
Year2020
PublisherIEEE
Place of PublicationUSA
ISSN1536-1233
eISSN1558-0660
LanguagesEnglish-United States

Last updated on 2020-03-12 at 23:08