Throughput-Optimal Multi-Link Access for Wi-Fi 7 via Multi-Agent Reinforcement Learning

Multi-link operation (MLO) is one of the pivotal new features in the upcoming IEEE 802.11be Wi-Fi 7 networks. To break the performance limit of traditional random-access-based Wi-Fi, we propose a novel distributed multi-link access scheme for Wi-Fi 7, leveraging the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. Specifically, a novel parameter, denoted as access opportunity, is introduced as the state transition time step in the decentralized partially observable Markov decision process, which enables the unified modeling of multiple links with varying transmission rates. With the proposed scheme, agents trained in a specific single-link network environment can be directly deployed in multi-link scenarios with varying link transmission parameters, significantly reducing training complexity. The proposed scheme undergoes evaluation in diverse network scenarios, which outperforms the throughput limit of standard multi-link Wi-Fi networks by up to 23.9%, ensures fairness among devices and is robust to environment dynamics.