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