用多智能体强化学习优化Wi-Fi并发传输，提升网络效率与公平性。
Coordinated Multi-Armed Bandits for Improved Spatial Reuse in Wi-Fi
- 设计多智能体老虎机算法协同调节干扰参数，实现跨网络智能调度。
- 平均吞吐量提升15%，最低吞吐提高210%，延迟低于3毫秒。
- 适合研究下一代Wi-Fi智能调度的科研人员和工程师参考。
多接入点协调（MAPC）与人工智能/机器学习（AI/ML）被认为是未来Wi-Fi（如即将推出的IEEE 802.11bn，即Wi-Fi 8）的关键特征。本文提出一种基于在线学习的协同方案，用于优化空间复用（SR），该技术通过调整包检测（PD）阈值和发射功率，允许多个设备在控制干扰的前提下同时传输。特别地，我们采用多智能体多臂赌博机（MA-MAB）框架，让来自不同网络的多个决策智能体在共存环境下协同配置SR参数，并研究多种算法与奖励共享机制。我们在广泛使用的Wi-Fi仿真器Komondor上评估了多种MA-MAB实现方式，结果表明：由协同MAB驱动的AI原生空间复用可显著提升网络性能——平均吞吐量提升15%，网络中最小吞吐量提高210%，最大接入延迟保持在3毫秒以下。
原文摘要 · Abstract (English)
Multi-Access Point Coordination (MAPC) and Artificial Intelligence and Machine Learning (AI/ML) are expected to be key features in future Wi-Fi, such as the forthcoming IEEE 802.11bn (Wi-Fi~8) and beyond. In this paper, we explore a coordinated solution based on online learning to drive the optimization of Spatial Reuse (SR), a method that allows multiple devices to perform simultaneous transmissions by controlling interference through Packet Detect (PD) adjustment and transmit power control. In particular, we focus on a Multi-Agent Multi-Armed Bandit (MA-MAB) setting, where multiple decision-making agents concurrently configure SR parameters from coexisting networks by leveraging the MAPC framework, and study various algorithms and reward-sharing mechanisms. We evaluate different MA-MAB implementations using Komondor, a well-adopted Wi-Fi simulator, and demonstrate that AI-native SR enabled by coordinated MABs can improve the network performance over current Wi-Fi operation: mean throughput increases by 15%, fairness is improved by increasing the minimum throughput across the network by 210%, while the maximum access delay is kept below 3 ms.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。