—Wireless communication networks provide a critical infrastructure, particularly in emergency situations due to disruptive events such as natural disasters or terrorist attacks. However, in these kinds of scenarios part of the network may no longer be operational and a traffic hotspot may emerge, which may result in coverage and/or capacity issues. Deploying self-steering drone-mounted base stations offers a potential method to quickly restore coverage and/or provide capacity relief in such situations, but appropriate positioning is crucial in order for a drone base station to be truly effective. Motivated by that challenge, we propose a data-driven algorithm to optimize the position of a drone base station in a scenario with a site failure and emergence of a traffic hotspot. We demonstrate that the use of a drone, when properly positioned, yields significant performance gains, and that our algorithm outperforms benchmark mechanisms in a wide range of scenarios. In addition, we show that our algorithm is able to find a near-optimal position for the drone in a reasonable amount of time, and even has the ability to track the optimal position in case of a moving hotspot.

The use of drone base stations offers an agile mechanism to safeguard coverage and provide capacity relief when cellular networks are under stress. Such stress conditions can occur for example in case of special events with massive crowds or network outages. In this paper we focus on a disaster scenario with emergence of a hotspot, and analyze the impact of the drone position (altitude, horizontal position) and selection bias on the network performance. We determine the optimal settings of these control parameters as a function of the hotspot location, and demonstrate that the optimized values can drastically reduce the fraction of failed calls.