Decentralized Spatial Reuse Optimization in Wi-Fi: An Internal Regret Minimization Approach

TL;DR

This paper presents a decentralized learning algorithm based on internal regret minimization for optimizing spatial reuse in dense Wi-Fi networks, achieving near-optimal performance without centralized coordination.

Contribution

It introduces a novel decentralized regret-matching algorithm that guides agents toward correlated equilibria, improving spatial reuse optimization in Wi-Fi without heavy signaling.

Findings

Outperforms standard selfish approaches in simulations.

Achieves near-optimal global performance.

Reduces need for centralized coordination.

Abstract

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).