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 less links if that contributes to reduce 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 difficult -- if not almost impossible -- for each individual 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 train models across multiple distributed agents without exchanging data, to collaboratively learn the 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 a better throughput fairness, and so a higher reliability -- because it allows the different BSSs to find a link allocation strategy which maximizes the minimum achieved data rate -- compared to fixed, random and RL-based MLO-LA schemes.

翻译：下一代Wi-Fi网络有望引入多链路操作（MLO）等新特性，以同时实现更高吞吐量和更低时延。然而，由于可用信道数量有限，一组竞争的基本服务集（BSS）使用多条链路可能导致更高的干扰和信道竞争，从而可能降低性能和可靠性。在这种情况下，若减少使用链路有助于缓解信道访问竞争，则所有竞争BSS减少链路使用可能是更优选择。近年来，强化学习（RL）已展现出其在优化无线网络资源分配方面的潜力。然而，每个无线网络的独立运行使得单个网络难以（甚至几乎不可能）学习到良好的配置。为解决此问题，本文提出采用联邦强化学习（FRL）框架——一种无需交换数据即可在多个分布式智能体间协同训练模型的机器学习方法——通过一组相邻BSS协作学习最优MLO链路分配（LA）策略。仿真结果表明，与固定、随机及基于RL的MLO-LA方案相比，基于FRL的分布式MLO-LA策略实现了更好的吞吐量公平性，从而具有更高可靠性——因为它使不同BSS能够找到最大化最小已实现数据速率的链路分配策略。