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).

翻译：空间复用（SR）是一种通过允许同时传输来提高密集IEEE 802.11部署中频谱效率的经济有效技术。然而，由于缺乏全局状态信息，在不同基本服务集（BSS）之间对SR参数——发射功率和载波侦听阈值（CST）——进行去中心化优化具有挑战性。此外，多个智能体的并发操作创造了一个高度非平稳的环境，通常导致次优的全局配置（例如，默认使用最大可能的发射功率）。为了克服这些限制，本文引入了一种基于遗憾匹配的去中心化学习算法，其理论基础是内部遗憾最小化。与通常收敛到低效纳什均衡（NE）的标准去中心化“自私”方法不同，内部遗憾最小化引导竞争智能体走向相关均衡（CE），从而在没有显式通信的情况下有效地模拟协调。通过仿真结果，我们展示了所提出方法的优越性及其达到接近最优全局性能的能力。这些结果证实了可扩展去中心化解决方案尚未释放的潜力，并对新兴集中式解决方案（如多接入点协调（MAPC））所需的大量信令开销和架构复杂性提出了质疑。