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

2022/06/08 by Mateus P. Mota, Mota, Mateus P., Álvaro Valcarce +3 · 1 citation
Engineering · Computer Science · #Full-Duplex Wireless Communications #Energy Harvesting in Wireless Networks #Wireless Networks and Protocols

paper · pdf · doi:10.48550/arxiv.2206.03844

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.

Cited by

• LLM-Empowered Agentic MAC Protocols: A Dynamic Stackelberg Game Approach

Related

• Heterogeneous Multi-Agent Reinforcement Learning for Distributed Channel Access in WLANs
• Towards Multi-agent Reinforcement Learning for Wireless Network Protocol\n Synthesis
• Consensus-based Decentralized Multi-agent Reinforcement Learning for Random Access Network Optimization
• On Learning Intrinsic Rewards for Faster Multi-Agent Reinforcement Learning based MAC Protocol Design in 6G Wireless Networks
• Scalable Multi-agent Reinforcement Learning Algorithm for Wireless Networks