Learning Multi-Access Point Coordination in Agentic AI Wi-Fi with Large Language Models

2025-11-25

AI Summary

Existing MAPC protocols rely on static rules, rendering them ill-suited to dynamic interference and topology changes in high-density Wi-Fi environments. This paper proposes the first large language model (LLM)-based autonomous multi-access-point (AP) coordination framework, introducing LLM agents into wireless resource management. The method employs natural-language-driven policy negotiation, updatable memory, environment-feedback closed loops, and distributed collaborative reasoning to enable real-time, adaptive inter-AP decision-making. It replaces handcrafted rules with cognitive workflows that explicitly model network coordination as a reasoning process. Simulation results in representative high-density scenarios demonstrate throughput gains of 23.6%–41.2% over state-of-the-art spatial reuse schemes, while exhibiting strong robustness against time-varying channels and topology perturbations. These results validate the feasibility and superiority of LLM-powered intelligent, cooperative wireless networking.

Technology Category

• Multiagent Systems: Coordination and Collaboration
• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Developing adaptive multi-access point coordination for dense Wi-Fi networks
• Overcoming static protocol limitations in dynamic network conditions
• Enabling real-time collaborative reasoning through autonomous AI agents

Innovation

• LLM agents enable real-time AP coordination
• Cognitive workflow uses dialogue for network adaptation
• Agents leverage memory and reflection for decisions