通过内部后悔最小化，让无线网络自主优化频谱复用效率。
Decentralized Spatial Reuse Optimization in Wi-Fi: An Internal Regret Minimization Approach
- 采用内部后悔最小化算法实现无通信的分布式协同决策
- 仿真显示可逼近最优全局性能，避免低效纳什均衡
- 适合大规模密集部署场景，替代复杂中心化协调方案
空间复用（SR）是一种在密集的IEEE 802.11网络中提升频谱效率的成本效益技术，允许不同基本服务集（BSS）同时传输。然而，由于缺乏全局状态信息，各BSS间对发射功率和载波侦测阈值（CST）的去中心化优化面临挑战。多代理并发操作导致环境高度非平稳，常出现次优配置（如默认使用最大发射功率）。本文提出一种基于后悔匹配的去中心化学习算法，以内部后悔最小化为理论基础。不同于通常收敛至低效纳什均衡的“自私”方法，该算法引导竞争代理趋向相关均衡（CE），在无需显式通信的情况下实现有效协同。仿真结果表明，所提方法显著优于传统方案，能够达到近似最优的全局性能。这些结果揭示了可扩展去中心化解决方案尚未释放的潜力，并质疑了新兴集中式方案（如多接入点协调，MAPC）所需的高开销信令与架构复杂性。
原文摘要 · Abstract (English)
Spatial Reuse (SR) is a cost-effective technique for improving spectral efficiency in dense IEEE 802.11 deployments by enabling simultaneous transmissions. However, the decentralized optimization of SR parameters -- transmission power and Carrier Sensing Threshold (CST) -- across different Basic Service Sets (BSSs) is challenging due to the lack of global state information. In addition, the concurrent operation of multiple agents creates a highly non-stationary environment, often resulting in suboptimal global configurations (e.g., using the maximum possible transmission power by default). To overcome these limitations, this paper introduces a decentralized learning algorithm based on regret-matching, grounded in internal regret minimization. Unlike standard decentralized ``selfish'' approaches that often converge to inefficient Nash Equilibria (NE), internal regret minimization guides competing agents toward Correlated Equilibria (CE), effectively mimicking coordination without explicit communication. Through simulation results, we showcase the superiority of our proposed approach and its ability to reach near-optimal global performance. These results confirm the not-yet-unleashed potential of scalable decentralized solutions and question the need for the heavy signaling overheads and architectural complexity associated with emerging centralized solutions like Multi-Access Point Coordination (MAPC).
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