Scalable Joint Learning of Wireless Multiple-Access Policies and their Signaling
This addresses scalability issues in MARL for wireless communication, offering a novel approach to improve network efficiency, though it is incremental in applying MARL to this specific domain.
The paper tackles the problem of learning channel access policies and signaling in wireless multiple-access networks using a multi-agent reinforcement learning (MARL) framework, achieving superior goodput and low collision rates compared to baselines, even under high traffic conditions.
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.