用大模型让多个Wi-Fi接入点自主协商，实时优化网络性能。
Learning Multi-Access Point Coordination in Agentic AI Wi-Fi with Large Language Models
- 每个接入点作为大模型智能体，通过自然语言对话协同决策。
- 在动态干扰环境下，吞吐量显著优于现有最优方案。
- 适合研究智能无线网络与AI驱动的通信协议的学者使用。
多接入点协调（MAPC）是提升密集重叠服务集环境下下一代Wi-Fi吞吐量的关键技术。然而，现有MAPC协议依赖静态、协议定义的规则，难以适应干扰水平和网络拓扑变化等动态条件。为此，我们提出一种新型代理式人工智能Wi-Fi框架：每个接入点被建模为一个自主的大语言模型智能体，通过实时协作推理网络状态并协商自适应协调策略。这种动态协作通过认知工作流实现，使智能体能够进行自然语言对话，利用集成记忆、反思和工具使用，将决策基于过往经验与环境反馈。全面的仿真结果表明，该代理框架能有效适应多样化动态网络环境，在吞吐量上显著优于当前最先进的空间复用基线，验证了其作为未来智能无线网络鲁棒解决方案的潜力。
原文摘要 · Abstract (English)
Multi-access point coordination (MAPC) is a key technology for enhancing throughput in next-generation Wi-Fi within dense overlapping basic service sets. However, existing MAPC protocols rely on static, protocol-defined rules, which limits their ability to adapt to dynamic network conditions such as varying interference levels and topologies. To address this limitation, we propose a novel Agentic AI Wi-Fi framework where each access point, modeled as an autonomous large language model agent, collaboratively reasons about the network state and negotiates adaptive coordination strategies in real time. This dynamic collaboration is achieved through a cognitive workflow that enables the agents to engage in natural language dialogue, leveraging integrated memory, reflection, and tool use to ground their decisions in past experience and environmental feedback. Comprehensive simulation results demonstrate that our agentic framework successfully learns to adapt to diverse and dynamic network environments, significantly outperforming the state-of-the-art spatial reuse baseline and validating its potential as a robust and intelligent solution for future wireless networks.
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