Mitigating starvation in dense WLANs: A multi-armed Bandit solution

With the recent 802.11ax amendment to the IEEE standard commercialized as Wi-Fi 6, WLANs have the potential to greatly improve the spatial reuse of radio channels. This resorts to the new ability for APs (Access Points) to dynamically modify their transmission power as well as the signal energy threshold beyond which they consider the radio channel to be free or busy. In general, selecting adequate values for these parameters is complex because of (i) the high dimensionality of the problem and (ii) the uncertainty of the radio environment. To overcome these difficulties, we frame this problem as a MAB (Multi-Armed Bandit) problem and propose an efficient and robust solution using Thompson sampling, an original sampling of WLAN configurations, and a tailor-made reward function. We evaluate the efficiency of our solution as well as several other ones with scenarios inspired by real-life WLANs' deployments using the network simulator ns-3. The numerical results show the ability of our solution along with its superiority over the others at finding adequate parameterization at each AP thereby significantly improving the overall performance of WLANs.