  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 environment-aware
learning. While the former stands for pure individual behavior, the second one
considers the performance experienced by surrounding networks, thus taking into
account the impact of individual actions on the environment. Through these two
strategies we delve into practical issues of applying MABs in wireless
networks, such as convergence guarantees or adversarial effects. Our simulation
results illustrate the potential of the proposed solutions for enabling SR in
future WLANs. We show that substantial improvements on network performance can
be achieved regarding throughput and fairness.
