LLM4MAC: An LLM-Driven Reinforcement Learning Framework for MAC Protocol Emergence

Future 6G networks require agile medium access control (MAC) protocols for dynamic conditions. Since traditional multi-agent reinforcement learning (MARL) falters with fluctuating agent numbers and typical LLM applications lack exploratory power for protocol emergence, we synergize LLMs with RL to propose LLM4MAC, overcoming these limitations. By reformulating uplink data transmission scheduling as a semantics-generalized partially observable Markov game (POMG), LLM4MAC encodes network operations in natural language and utilizes proximal policy optimization (PPO) to ensure continuous alignment with the evolving network dynamics. A structured identity embedding (SIE) mechanism further enables robust coordination among heterogeneous agents. Extensive simulations demonstrate that on top of a compact LLM, which is purposefully selected to balance performance with resource efficiency, the protocol emerging from LLM4MAC outperforms comparative baselines in throughput and generalization.