标题： IEEE 802.11bn 多接入点协调的空间复用与分层多臂老虎机 显示英文标题

标题： IEEE 802.11bn Multi-AP Coordinated Spatial Reuse with Hierarchical Multi-Armed Bandits

摘要： 多接入点（AP）之间的协调对于IEEE 802.11bn（Wi-Fi 8）在密集网络中管理竞争至关重要。 这封信探讨了协调空间复用（C-SR）的好处，并提出使用强化学习来优化C-SR组选择。 我们开发了一个分层多臂老虎机（MAB）框架，该框架在各种网络拓扑中高效选择同时传输的AP，展示了强化学习在Wi-Fi环境中的潜力。 在研究的几种MAB算法中，我们确定上置信界（UCB）特别有效，具有快速收敛、适应变化和持续性能的优势。 显示英文摘要

摘要： Coordination among multiple access points (APs) is integral to IEEE 802.11bn (Wi-Fi 8) for managing contention in dense networks. This letter explores the benefits of Coordinated Spatial Reuse (C-SR) and proposes the use of reinforcement learning to optimize C-SR group selection. We develop a hierarchical multi-armed bandit (MAB) framework that efficiently selects APs for simultaneous transmissions across various network topologies, demonstrating reinforcement learning's promise in Wi-Fi settings. Among several MAB algorithms studied, we identify the upper confidence bound (UCB) as particularly effective, offering rapid convergence, adaptability to changes, and sustained performance.