← 研踪 PaperTrailarXiv:2611.16634 · v1
Machine Learning for Scalable Multi-AP Coordination in IEEE 802.11bn Networks
首次提交 2026-11-12 19:07
版本更新 2026-11-12 19:07
雷达观测 2026-11-13 14:15
摘要
As Wi-Fi deployments become denser, fully distributed random channel access no longer guarantees the required quality of service. The multi-access point coordination (MAPC) framework introduced in IEEE~802.11bn lets neighboring access points (APs) cooperate, but choosing which APs transmit together and how to configure them remains an open, computationally hard problem. We develop and validate a stack of machine learning (ML) coordination methods, progressing from lightweight hierarchical multi-armed bandits (H-MABs) to a generative pipeline built on graph neural networks (GNNs) and flow matching (FM). The methods are evaluated in simulation and on a hardware testbed, improving throughput over legacy access and scaling coordination to dozens of APs where analytical and search-based methods become intractable.
期刊信息：The 28th International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM 2026)
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v1 · 2026-11-12 19:07 · 最新观测版本
Machine Learning for Scalable Multi-AP Coordination in IEEE 802.11bn Networks
As Wi-Fi deployments become denser, fully distributed random channel access no longer guarantees the required quality of service. The multi-access point coordination (MAPC) framework introduced in IEEE~802.11bn lets neighboring access points (APs) cooperate, but choosing which APs transmit together and how to configure them remains an open, computationally hard problem. We develop and validate a stack of machine learning (ML) coordination methods, progressing from lightweight hierarchical multi-armed bandits (H-MABs) to a generative pipeline built on graph neural networks (GNNs) and flow matching (FM). The methods are evaluated in simulation and on a hardware testbed, improving throughput over legacy access and scaling coordination to dozens of APs where analytical and search-based methods become intractable.
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