FM4WiFi：超Wi-Fi 8密集部署场景下多接入点协调的流匹配方法
FM4WiFi: Flow Matching for Multi-AP Coordination in Dense Deployments of Beyond Wi-Fi 8 Networks
- AGH University of Krakow（克拉科夫AGH大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对超Wi-Fi 8密集部署中多AP协调的可扩展性问题，提出FM4WiFi生成式ML流程，通过自编码器、流匹配模型和替代速率预测器实现高效Co-SR配置，性能优异且可扩展至30+AP。
AI中文摘要：
Wi-Fi网络正从随机信道访问转向接入点（AP）间紧密协调的运行，这一转变体现在Wi-Fi 8的多AP协调（MAPC）规范中。然而，当前MAPC规范将合作限制在AP对之间，从根本上限制了密集部署场景下可获得的性能增益，这就要求超Wi-Fi 8系统具备可扩展的全网协调能力。本研究聚焦于协调空间复用（Co-SR），即AP以降低的功率同时进行传输。有效的Co-SR需要联合选择和配置AP-站传输，但现有方法根本不具备可扩展性：它们依赖大量信令、收敛缓慢、存在不切实际的假设，且计算时间会随网络规模扩大而激增。我们提出FM4WiFi，这是一种生成式机器学习流程，可通过单次推理步骤生成高质量的Co-SR配置，从而解决上述限制。FM4WiFi整合了三个部分：（i）学习网络状态紧凑潜在表示的自编码器；（ii）合成可行Co-SR配置（包括现有工作缺失的速率控制）的流匹配生成模型；（iii）替代速率预测器，可在不依赖实时系统或数字孪生的情况下快速评估大规模Co-SR候选方案。通过广泛的评估（包括实验验证），FM4WiFi在中大规模场景下的性能与最先进的基准方法相当或更优，且可扩展至30个以上AP，推理时间在秒级以内。大量消融研究验证了每个建模和优化选择的有效性。
英文摘要：
Wi-Fi networks are moving beyond random channel access toward tightly coordinated operation across access points (APs), a shift reflected in Wi-Fi 8's multi-AP coordination (MAPC). However, the current MAPC specification restricts cooperation to AP pairs, fundamentally limiting the gains achievable in dense deployments and calling for scalable, network-wide coordination in beyond Wi-Fi 8 systems. We target coordinated spatial reuse (Co-SR), where APs transmit concurrently at reduced power. Effective Co-SR demands joint selection and configuration of AP-station transmissions, yet existing approaches simply do not scale: they rely on heavy signaling, slow convergence, unrealistic assumptions, and often require computation time that explodes with network size. We introduce FM4WiFi, a generative ML pipeline that addresses these limitations by producing high-quality Co-SR configurations in a single inference step. FM4WiFi integrates (i) an autoencoder that learns compact latent representations of network states, (ii) a flow-matching generative model that synthesizes feasible Co-SR configurations (including rate control, absent from prior work), and (iii) a surrogate rate predictor that allows rapid, large-scale Co-SR candidate evaluation without dependence on a live system or digital twin. Across extensive evaluations (including experimental validation), FM4WiFi matches or exceeds state-of-the-art baselines at medium-to-large scales and scales to 30+ APs with sub-second inference. Extensive ablation studies validate each modeling and optimization choice.
