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

2026-02-09

AI Summary

This work addresses the challenge of inefficient spatial reuse in dense Wi-Fi deployments, where individual basic service sets (BSSs) lack global information and struggle to cooperatively optimize parameters such as transmit power and carrier sense thresholds, often converging to suboptimal Nash equilibria. The paper introduces internal regret minimization to this domain for the first time, proposing a decentralized learning algorithm based on regret matching that guides multiple agents toward correlated equilibria without explicit communication. By integrating game-theoretic principles with lightweight reinforcement learning, the method dynamically tunes key parameters while avoiding complex signaling overhead. Experimental results demonstrate that the approach significantly outperforms existing decentralized schemes and achieves performance close to the global optimum, thereby validating the effectiveness and potential of decentralized cooperative optimization for Wi-Fi spatial reuse.

Technology Category

• Multiagent Systems: Mechanism Design
• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Content-based information diffusion

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).

Problem

• Spatial Reuse
• Decentralized Optimization
• Carrier Sensing Threshold
• Non-stationary Environment
• Spectral Efficiency

Innovation

• Internal Regret Minimization
• Decentralized Learning
• Spatial Reuse
• Correlated Equilibrium
• Wi-Fi Optimization