  Enterprise Wireless Local Area Networks (WLANs) consist of multiple Access
Points (APs) covering a given area. Finding a suitable network configuration
able to maximize the performance of enterprise WLANs is a challenging task
given the complex dependencies between APs and stations. Recently, in wireless
networking, the use of reinforcement learning techniques has emerged as an
effective solution to efficiently explore the impact of different network
configurations in the system performance, identifying those that provide better
performance. In this paper, we study if Multi-Armed Bandits (MABs) are able to
offer a feasible solution to the decentralized channel allocation and AP
selection problems in Enterprise WLAN scenarios. To do so, we empower APs and
stations with agents that, by means of implementing the Thompson sampling
algorithm, explore and learn which is the best channel to use, and which is the
best AP to associate, respectively. Our evaluation is performed over randomly
generated scenarios, which enclose different network topologies and traffic
loads. The presented results show that the proposed adaptive framework using
MABs outperform the static approach (i.e., using always the initial default
configuration, usually random) regardless of the network density and the
traffic requirements. Moreover, we show that the use of the proposed framework
reduces the performance variability between different scenarios. Results also
show that we achieve the same performance (or better) than static strategies
with less APs for the same number of stations. Finally, special attention is
placed on how the agents interact. Even if the agents operate in a completely
independent manner, their decisions have interrelated effects, as they take
actions over the same set of channel resources.
