TL;DR
This paper explores the use of Multi-Armed Bandits for decentralized spatial reuse in WLANs, demonstrating potential improvements in throughput and fairness through adaptive learning strategies.
Contribution
It introduces two MAB-based strategies for decentralized channel and power adjustment in WLANs, addressing practical issues like convergence and environmental impact.
Findings
Significant throughput and fairness improvements achieved.
Two distinct learning strategies analyzed for practical deployment.
Simulation results validate the potential of MABs for WLAN spatial reuse.
Abstract
Spatial Reuse (SR) has recently gained attention to maximize the performance of IEEE 802.11 Wireless Local Area Networks (WLANs). Decentralized mechanisms are expected to be key in the development of SR solutions for next-generation WLANs, since many deployments are characterized by being uncoordinated by nature. However, the potential of decentralized mechanisms is limited by the significant lack of knowledge with respect to the overall wireless environment. To shed some light on this subject, we show the main considerations and possibilities of applying online learning to address the SR problem in uncoordinated WLANs. In particular, we provide a solution based on Multi-Armed Bandits (MABs) whereby independent WLANs dynamically adjust their frequency channel, transmit power and sensitivity threshold. To that purpose, we provide two different strategies, which refer to selfish and…
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