企业无线局域网中用于分散式AP选择的多臂强盗
Multi-Armed Bandits for Decentralized AP selection in Enterprise WLANs
摘要：
WiFi密集化导致存在多个重叠的覆盖区域，这允许用户站（STA）在不同的接入点（AP）之间进行选择。标准的WiFi关联方法使得STA选择具有最强信号的AP，这在许多情况下导致一些AP的利用率不足而使其他AP过度拥挤。为了缓解这种情况，可以使用诸如多臂强盗（Multi-Armed Bandits）的强化学习技术来动态地学习AP与STA之间的最佳映射，并且因此相应地在可用AP之间重新分布STA。这是一个特别具有挑战性的问题，因为由给定STA观察到的网络响应取决于其他STA的行为，因此在没有网络全局视图的情况下很难预测。在本文中，我们专注于解决这个问题，在分散的方式，STA独立地探索其覆盖范围内的不同AP，并选择一个更好地满足他们的需求。要做到这一点，我们提出了一种新的方法，称为粘性的贪婪epsilon-greedy，当找到合适的AP时停止探索，只有在几个不满意的关联轮后才恢复探索。通过这种方法，我们大大减少了网络响应动态，提高了STA的能力，找到一个更快的解决方案，以及实现更有效地利用网络资源。我们调查的情况下（AP和STA的位置，流量负载和信道分配策略）的特性如何影响学习过程和可实现的性能。我们还表明，并不是所有的STA都必须实现所提出的解决方案，以提高其性能。最后，我们研究的情况下，车站逐步到达系统，所考虑的方法也是适合在这样一个非平稳设置。
英文摘要：
WiFi densification leads to the existence of multiple overlapping coverage areas, which allows user stations (STAs) to choose between different Access Points (APs). The standard WiFi association method makes the STAs select the AP with the strongest signal, which in many cases leads to underutilization of some APs while overcrowding others. To mitigate this situation, Reinforcement Learning techniques such as Multi-Armed Bandits can be used to dynamically learn the optimal mapping between APs and STAs, and so redistribute the STAs among the available APs accordingly. This is an especially challenging problem since the network response observed by a given STA depends on the behavior of the others, and so it is very difficult to predict without a global view of the network. In this paper, we focus on solving this problem in a decentralized way, where STAs independently explore the different APs inside their coverage range, and select the one that better satisfy their needs. To do it, we propose a novel approach called Opportunistic epsilon-greedy with Stickiness that halts the exploration when a suitable AP is found, only resuming the exploration after several unsatisfactory association rounds. With this approach, we reduce significantly the network response dynamics, improving the ability of the STAs to find a solution faster, as well as achieving a more efficient use of the network resources. We investigate how the characteristics of the scenario (position of the APs and STAs, traffic loads, and channel allocation strategies) impact the learning process and the achievable performance. We also show that not all the STAs have to implement the proposed solution to improve their performance. Finally, we study the case where stations arrive progressively to the system, showing that the considered approach is also suitable in such a non-stationary set-up.
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