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This paper investigates a new class of carrier-sense multiple access (CSMA)protocols that employ deep reinforcement learning (DRL) techniques, referred toas carrier-sense deep-reinforcement learning multiple access (CS-DLMA). Thegoal of CS-DLMA is to enable efficient and equitable spectrum sharing among agroup of co-located heterogeneous wireless networks. Existing CSMA protocols,such as the medium access control (MAC) of WiFi, are designed for a homogeneousnetwork in which all nodes adopt the same protocol. Such protocols suffer fromsevere performance degradation in a heterogeneous environment where there arenodes adopting other MAC protocols. CS-DLMA aims to circumvent this problem bymaking use of DRL. In particular, this paper adopts alpha-fairness as thegeneral objective of CS-DLMA. With alpha-fairness, CS-DLMA can achieve a rangeof different objectives when coexisting with other MACs by changing the valueof alpha. A salient feature of CS-DLMA is that it can achieve these objectiveswithout knowing the coexisting MACs through a learning process based on DRL.The underpinning DRL technique in CS-DLMA is deep Q-network (DQN). However, theconventional DQN algorithms are not suitable for CS-DLMA due to their uniformtime-step assumption. In CSMA protocols, time steps are non-uniform in that thetime duration required for carrier sensing is smaller than the duration of datatransmission. This paper introduces a non-uniform time-step formulation of DQNto address this issue. Our simulation results show that CS-DLMA can achieve thegeneral alpha-fairness objective when coexisting with TDMA, ALOHA, and WiFiprotocols by adjusting its own transmission strategy. Interestingly, we alsofind that CS-DLMA is more Pareto efficient than other CSMA protocols whencoexisting with WiFi.

Non-Uniform Time-Step Deep Q-Network for Carrier-Sense Multiple Access in Heterogeneous Wireless Networks