mlsanityData to 14 Oct 2026

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

arXiv 2510.10895 ↗First posted 13 Oct 2025cs.AIcited by 0

On the map: LLM agents and tool use → Reinforcement learning for LLM agents

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reinforcement learning for LLM agents, one of 16 directions in LLM agents and tool use. Builds on 28 of its 55 references that are in the map. First posted on arXiv 13 Oct 2025.

Builds on

28 of 55 references.

References · 28

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2023Grounding Large Language Models in Interactive Environments with Online Reinforcement Learningcited by 195
  3. 2024WirelessLLM: Empowering Large Language Models Towards Wireless Intelligencecited by 44
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  5. 2014On the Characterization of Local Nash Equilibria in Continuous Gamescited by 22
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  7. 2020Towards Joint Learning of Optimal MAC Signaling and Wireless Channel Accesscited by 18
  8. 2021The Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learningcited by 15
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  10. 2022Towards Semantic Communication Protocols: A Probabilistic Logic Perspectivecited by 10
  11. 2023Towards Semantic Communication Protocols for 6G: From Protocol Learning to Language-Oriented Approachescited by 7
  12. 2022Scalable Joint Learning of Wireless Multiple-Access Policies and their Signalingcited by 3
16 more
  1. 2023Learning Random Access Schemes for Massive Machine-Type Communication with MARLcited by 3
  2. 2024Knowledge Distillation from Language-Oriented to Emergent Communication for Multi-Agent Remote Controlcited by 3
  3. 2025CP-AgentNet: Autonomous and Explainable Communication Protocol Design Using Generative Agentscited by 3
  4. 2024ML Framework for Wireless MAC Protocol Designcited by 2
  5. 2025LLM4MAC: An LLM-Driven Reinforcement Learning Framework for MAC Protocol Emergencecited by 2
  6. 2025LLM-Based Emulation of the Radio Resource Control Layer: Towards AI-Native RAN Protocolscited by 2
  7. 2025Resilient LLM-Empowered Semantic MAC Protocols via Zero-Shot Adaptation and Knowledge Distillationcited by 2
  8. 2025Decentralized Consensus Inference-based Hierarchical Reinforcement Learning for Multi-Constrained UAV Pursuit-Evasion Gamecited by 2
  9. 2022Multi-Agent Feedback Enabled Neural Networks for Intelligent Communicationscited by 1
  10. 2024Balancing Performance and Cost for Two-Hop Cooperative Communications: Stackelberg Game and Distributed Multi-Agent Reinforcement Learningcited by 1
  11. 2024Optimizing Wireless Discontinuous Reception via MAC Signaling Learningcited by 1
  12. 2024Wireless MAC Protocol Synthesis and Optimization with Multi-Agent Distributed Reinforcement Learningcited by 1
  13. 2025Learning in Conjectural Stackelberg Gamescited by 1
  14. 2025ProtocolLLM: RTL Benchmark for SystemVerilog Generation of Communication Protocolscited by 1
  15. 2025Resilient-native and Intelligent NextG Systemscited by 1
  16. 2025WiLLM: an Open Framework for LLM Services over Wireless Systemscited by 1

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  3. 2026ComAgent: Multi-LLM based Agentic AI Empowered Intelligent Wireless Networks
  4. 2025Consensus-based Decentralized Multi-agent Reinforcement Learning for Random Access Network Optimization
  5. 2023Wireless Multi-Agent Generative AI: From Connected Intelligence to Collective Intelligence
  6. 2025Prompt-Tuned LLM-Augmented DRL for Dynamic O-RAN Network Slicing
  7. 2026LLM-Guided Communication for Cooperative Multi-Agent Reinforcement Learning
  8. 2024Heterogeneous Multi-Agent Reinforcement Learning for Distributed Channel Access in WLANs