用大模型+博弈论让无线网络自适应调整通信协议，无需重训。
LLM-Empowered Agentic MAC Protocols: A Dynamic Stackelberg Game Approach
- 基于大模型和动态博弈设计自适应通信协议
- 吞吐量提升77.6%，公平性改善65.2%
- 支持用户数波动，无需重新训练
介质访问控制（MAC）协议对无线网络至关重要，但传统方法依赖人工配置。尽管基于深度强化学习（DRL）的协议能优化特定任务性能，却普遍存在泛化能力差、抗扰动能力弱的问题，需耗费大量成本重新训练以应对动态环境。为解决此问题，我们提出一种基于博弈论的大型语言模型（LLM）赋能多智能体强化学习（MARL）框架，将基站与可变数量用户设备之间的上行传输建模为动态多追随者斯塔克尔伯格博弈（MFSG），捕捉网络天然的层级结构。在此博弈中，通过近端策略优化（PPO）协调的LLM驱动智能体，根据网络动态生成自适应语义化的MAC协议，采用协议动作语法（PAG）保障过程可靠性与效率。进一步分析了该系统在追随者变化下统一策略的学习动态，验证了斯塔克尔伯格均衡的存在性与收敛性。仿真结果表明，本框架相较传统基线实现77.6%的吞吐量提升和65.2%的公平性改进，且在用户数量波动时仍具备优异泛化能力，无需重训或架构调整。
原文摘要 · Abstract (English)
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 generalizability and resilience, demanding costly retraining to adapt to dynamic environments. To 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. Besides, our framework generalizes excellently to a fluctuating number of users without requiring retraining or architectural changes.
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