Enterprise Wireless Local Area Networks (WLANs) consist of multiple Access Points (APs) covering a given area. In these networks, interference is mitigated by allocating different channels to neighboring APs. Besides, stations are allowed to associate to any AP in the network, selecting by default the one from which receive higher power, even if it is not the best option in terms of the network performance.

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. Also, results 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.

企业无线局域网（WLAN）由覆盖给定区域的多个接入点（AP）组成。在这些网络中，通过为相邻的AP分配不同的信道来减轻干扰。此外，允许站与网络中的任何AP关联，默认情况下，从网络中选择接收功率更高的AP，即使就网络性能而言，这并不是最佳选择。

考虑到AP和站点之间的复杂依赖关系，找到一种能够最大化企业WLAN性能的合适网络配置是一项艰巨的任务。最近，在无线网络中，使用强化学习技术已成为一种有效的解决方案，可以有效地探索不同网络配置对系统性能的影响，从而确定提供更好性能的网络。

在本文中，我们研究了多武装强盗（MAB）是否能够为企业WLAN场景中的分散信道分配和AP选择问题提供可行的解决方案。为此，我们通过配置汤普森采样算法为AP和站赋予代理，从而分别探究和了解哪个是使用的最佳渠道，哪个是关联的最佳AP。我们的评估是在随机生成的场景中执行的，其中包含了不同的网络拓扑和流量负载。提出的结果表明，无论网络密度和流量需求如何，使用MAB的自适应框架都优于静态方法（即始终使用初始默认配置，通常是随机的）。此外，我们表明，所提出的框架的使用减少了不同场景之间的性能差异。同样，结果表明，对于相同数量的站点，我们使用较少的AP即可获得比静态策略相同（或更好）的性能。最后，要特别注意代理之间的交互方式。即使代理以完全独立的方式运行，它们的决策也具有相互关联的效果，因为它们在同一组通道资源上采取行动。