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

• URL: http://arxiv.org/abs/2206.03844v1
• Date: Wed, 8 Jun 2022 12:38:04 GMT
• Title: Scalable Joint Learning of Wireless Multiple-Access Policies and their Signaling
• Authors: Mateus P. Mota, Alvaro Valcarce, Jean-Marie Gorce
• Abstract summary: 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. Our framework achieves a superior performance in terms of goodput even in high traffic situations while maintaining a low collision rate.
• Score: 2.268853004164585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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