A hybrid MAC protocol that starts with LLM-generated control messages and switches to a distilled neural model after retraining improves resilience to user-count changes at lower compute than pure LLM inference.
LLM4MAC: An LLM-Driven Reinforcement Learning Framework for MAC Protocol Emergence
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abstract
With the advent of 6G systems, emerging hyper-connected ecosystems necessitate agile and adaptive medium access control (MAC) protocols to contend with network dynamics and diverse service requirements. We propose LLM4MAC, a novel framework that harnesses large language models (LLMs) within a reinforcement learning paradigm to drive MAC protocol emergence. By reformulating uplink data transmission scheduling as a semantics-generalized partially observable Markov game (POMG), LLM4MAC encodes network operations in natural language, while proximal policy optimization (PPO) ensures 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.
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Resilient LLM-Empowered Semantic MAC Protocols via Zero-Shot Adaptation and Knowledge Distillation
A hybrid MAC protocol that starts with LLM-generated control messages and switches to a distilled neural model after retraining improves resilience to user-count changes at lower compute than pure LLM inference.