Foundation Model-Aided Multi-Agent Reinforcement Learning for Wireless Random Access Network Optimization

Excerpt

arXiv:2610.07550v1 Announce Type: new Abstract: Random access (RA) is one of the most foundational medium access control (MAC) layer scheduling schemes for handling unpredictable data traffic from multiple terminals. While multi-agent reinforcement learning (MARL) has been explored to optimize RA-based wireless networks, its reliance on experience-driven, distributed policy learning incurs significant training overhead for each optimization task, limiting its feasibility in real-world applicatio