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Conference Papers Year : 2022

Scalable Joint Learning of Wireless Multiple-Access Policies and their Signaling

Abstract

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.

Dates and versions

hal-03963663 , version 1 (30-01-2023)

Identifiers

Cite

Mateus Mota, Alvaro Valcarce, Jean-Marie Gorce. Scalable Joint Learning of Wireless Multiple-Access Policies and their Signaling. 2022 IEEE 95th Vehicular Technology Conference (VTC2022-Spring), Jun 2022, Helsinki, Finland. pp.1-5, ⟨10.1109/VTC2022-Spring54318.2022.9860804⟩. ⟨hal-03963663⟩
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