A Federated Reinforcement Learning Framework for Link Activation in Multi-Link Wi-Fi Networks

Next-generation Wi-Fi networks are looking forward to introducing new features like multi-link operation (MLO) to both achieve higher throughput and lower latency. However, given the limited number of available channels, the use of multiple links by a group of contending Basic Service Sets (BSSs) can result in higher interference and channel contention, thus potentially leading to lower performance and reliability. In such a situation, it could be better for all contending BSSs to use fewer links if that contributes to reducing channel access contention. Recently, reinforcement learning (RL) has proven its potential for optimizing resource allocation in wireless networks. However, the independent operation of each wireless network makes it difficult - if not almost impossible- for each network to learn a good configuration. To solve this issue, in this paper, we propose the use of a Federated Reinforcement Learning (FRL) framework, i.e., a collaborative machine learning approach to training models across multiple distributed agents without exchanging data, to collaboratively learn the best MLO- Link Allocation (LA) strategy by a group of neighboring BSSs. The simulation results show that the FRL-based decentralized MLO-LA strategy achieves better throughput fairness, and so higher reliability -because it allows the different BSSs to find a link allocation strategy that maximizes the minimum achieved data rate- compared to fixed, random, and RL-based MLO-LA schemes.