LLM4MAC: An LLM-Driven Reinforcement Learning Framework for MAC Protocol Emergence
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916649534226432 |
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| author | Tan, Renxuan Li, Rongpeng Zhao, Zhifeng |
| author_facet | Tan, Renxuan Li, Rongpeng Zhao, Zhifeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08123 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLM4MAC: An LLM-Driven Reinforcement Learning Framework for MAC Protocol Emergence Tan, Renxuan Li, Rongpeng Zhao, Zhifeng Networking and Internet Architecture 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. |
| title | LLM4MAC: An LLM-Driven Reinforcement Learning Framework for MAC Protocol Emergence |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2503.08123 |