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

Abstract--Medium Access Control (MAC) protocols, essential for wireless networks, are typically manually configured. While deep reinforcement learning (DRL)-based protocols enhance task-specified network performance, they suffer from poor gener-alizability and resilience, demanding costly retraining to adapt to dynamic environments. T o overcome this limitation, we introduce a game-theoretic LLM-empowered multi-agent DRL (MARL) framework, in which the uplink transmission between a base station and a varying number of user equipments is modeled as a dynamic multi-follower Stackelberg game (MFSG), capturing the network's natural hierarchical structure. Within this game, LLM-driven agents, coordinated through proximal policy optimization (PPO), synthesize adaptive, semantic MAC protocols in response to network dynamics. Protocol action grammar (PAG) is employed to ensure the reliability and efficiency of this process. Under this system, we further analyze the existence and convergence behavior in terms of a Stackelberg equilibrium by studying the learning dynamics of LLM-empowered unified policies in response to changing followers. Simulations corroborate that our framework achieves a 77.6% greater throughput and a 65.2% fairness improvement over conventional baselines. He evolution towards next-generation (xG) wireless systems envisions artificial intelligence (AI)-native architectures wherein intelligent, resilient communication protocols autonomously emerge to manage unprecedented network dynamics [1]. Central to this vision is the medium access control (MAC) protocol, which orchestrates channel access among numerous nodes. As network topologies become increasingly varying and heterogeneous, the prevailing paradigm of designing static, human-engineered MAC protocols is rendered obsolete, necessitating protocol emergence solutions that can learn and adapt in real-time [2].