In this paper, we apply an multi-agent reinforcement learning (MARL)
framework allowing the base station (BS) and the user equipments (UEs) to
jointly learn a channel access policy and its signaling in a wireless multiple
access scenario. In this framework, the BS and UEs are reinforcement learning
(RL) agents that need to cooperate in order to deliver data. The comparison
with a contention-free and a contention-based baselines shows that our
framework achieves a superior performance in terms of goodput even in high
traffic situations while maintaining a low collision rate. The scalability of
the proposed method is studied, since it is a major problem in MARL and this
paper provides the first results in order to address it.

本文利用多智能体强化学习框架，让基站和用户设备一起学习在无线多点接入场景中的信道访问策略和信令，比较竞争自由和基于竞争的基线，结果显示我们的框架在高流量情况下保持低冲突率的同时，在吞吐量方面表现出色。同时本文研究了所提出方法的可扩展性，并为解决该问题提供了首个结果。