Coordinated Multi-Armed Bandits for Improved Spatial Reuse in Wi-Fi
This work addresses performance bottlenecks in Wi-Fi networks for users and devices by enabling more efficient spatial reuse, though it is incremental as it builds on existing MAPC and MAB frameworks.
The paper tackles the problem of optimizing Spatial Reuse in Wi-Fi networks to allow simultaneous transmissions by controlling interference, using a coordinated Multi-Agent Multi-Armed Bandit approach, and demonstrates improvements including a 15% increase in mean throughput and a 210% increase in minimum throughput while keeping maximum access delay below 3 ms.
Multi-Access Point Coordination (MAPC) and Artificial Intelligence and Machine Learning (AI/ML) are expected to be key features in future Wi-Fi, such as the forthcoming IEEE 802.11bn (Wi-Fi~8) and beyond. In this paper, we explore a coordinated solution based on online learning to drive the optimization of Spatial Reuse (SR), a method that allows multiple devices to perform simultaneous transmissions by controlling interference through Packet Detect (PD) adjustment and transmit power control. In particular, we focus on a Multi-Agent Multi-Armed Bandit (MA-MAB) setting, where multiple decision-making agents concurrently configure SR parameters from coexisting networks by leveraging the MAPC framework, and study various algorithms and reward-sharing mechanisms. We evaluate different MA-MAB implementations using Komondor, a well-adopted Wi-Fi simulator, and demonstrate that AI-native SR enabled by coordinated MABs can improve the network performance over current Wi-Fi operation: mean throughput increases by 15%, fairness is improved by increasing the minimum throughput across the network by 210%, while the maximum access delay is kept below 3 ms.