arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2511.20719cs.AIcs.ITeess.SPmath.IT
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在基于大语言模型的代理AI无线局域网中学习多接入点协调

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

School of Information Science and Engineering(信息科学与工程学院);Purple Mountain Laboratories(紫金山实验室);Wireless Technology Lab, 2012 Labs, Huawei Technologies Co., Ltd(无线技术实验室,2012实验室,华为技术有限公司)

Yifan Fan, Le Liang, Peng Liu, Xiao Li, Ziyang Guo, Qiao Lan, Shi Jin, Wen Tong

AI总结:

本文提出基于大语言模型的代理AI无线局域网框架,通过动态协作实现多接入点协调,提升无线网络性能。

摘要:

多接入点协调(MAPC)是提升下一代无线局域网在密集重叠基本服务区中的吞吐量的关键技术。然而,现有的MAPC协议依赖于静态、协议定义的规则,这限制了它们适应动态网络条件(如变化的干扰水平和拓扑结构)的能力。为了解决这一限制,我们提出了一种新的代理AI无线局域网框架,其中每个接入点被建模为一个自主的大语言模型代理,协作地推理网络状态并实时协商适应性协调策略。这种动态协作是通过一种认知工作流实现的,使代理能够进行自然语言对话,利用集成的记忆、反思和工具使用来使决策基于以往的经验和环境反馈。全面的仿真结果表明,我们的代理框架成功地学会了适应多样且动态的网络环境,显著优于最先进的空间重用基线,并验证了其作为未来无线网络稳健且智能解决方案的潜力。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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