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Spatial Reuse (SR) has recently gained attention to maximize the performanceof IEEE 802.11 Wireless Local Area Networks (WLANs). Decentralized mechanismsare expected to be key in the development of SR solutions for next-generationWLANs, since many deployments are characterized by being uncoordinated bynature. However, the potential of decentralized mechanisms is limited by thesignificant lack of knowledge with respect to the overall wireless environment.To shed some light on this subject, we show the main considerations andpossibilities of applying online learning to address the SR problem inuncoordinated WLANs. In particular, we provide a solution based on Multi-ArmedBandits (MABs) whereby independent WLANs dynamically adjust their frequencychannel, transmit power and sensitivity threshold. To that purpose, we providetwo different strategies, which refer to selfish and environment-awarelearning. While the former stands for pure individual behavior, the second oneconsiders the performance experienced by surrounding networks, thus taking intoaccount the impact of individual actions on the environment. Through these twostrategies we delve into practical issues of applying MABs in wirelessnetworks, such as convergence guarantees or adversarial effects. Our simulationresults illustrate the potential of the proposed solutions for enabling SR infuture WLANs. We show that substantial improvements on network performance canbe achieved regarding throughput and fairness.

Potential and Pitfalls of Multi-Armed Bandits for Decentralized Spatial Reuse in WLANs