Decentralized Spatial Reuse Optimization in Wi-Fi: An Internal Regret Minimization Approach
AI 摘要
提出基于内部后悔最小化的分布式算法，优化Wi-Fi网络中的空间复用，提升频谱效率。
主要贡献
- 提出了一种基于内部后悔最小化的分布式学习算法。
- 证明了该算法能够有效解决Wi-Fi网络中空间复用的优化问题。
- 实验结果表明该算法优于传统的“自私”算法，且接近最优性能。
方法论
采用基于后悔匹配的分布式学习算法，模拟协调机制，优化传输功率和CST参数。
原文摘要
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).