Multi-Agent Reinforcement Learning Based Uplink OFDMA for IEEE 802.11ax Networks

In the IEEE 802.11ax Wireless Local Area Networks (WLANs), Orthogonal Frequency Division Multiple Access (OFDMA) has been applied to enable the high-throughput WLAN amendment. However, with the growth of the number of devices, it is difficult for the Access Point (AP) to schedule uplink transmissions, which calls for an efficient access mechanism in the OFDMA uplink system. Based on Multi-Agent Proximal Policy Optimization (MAPPO), we propose a Mean-Field Multi-Agent Proximal Policy Optimization (MFMAPPO) algorithm to improve the throughput and guarantee the fairness. Motivated by the Mean-Field games (MFGs) theory, a novel global state and action design are proposed to ensure the convergence of MFMAPPO in the massive access scenario. The Multi-Critic Single-Policy (MCSP) architecture is deployed in the proposed MFMAPPO so that each agent can learn the optimal channel access strategy to improve the throughput while satisfying fairness requirement. Extensive simulation experiments are performed to show that the MFMAPPO algorithm 1) has low computational complexity that increases linearly with respect to the number of stations 2) achieves nearly optimal throughput and fairness performance in the massive access scenario, 3) can adapt to various diverse and dynamic traffic conditions without retraining, as well as the traffic condition different from training traffic.