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.

Drone base stations can help safeguard coverage and provide capacity relief when cellular networks are under stress. Examples of such stress scenarios are events with massive crowds or network outages. In this paper we focus on a disaster scenario with emergence of a traffic hotspot, where agile drone positioning and load management is a critical issue. In order to address this challenge, we propose and assess a data-driven algorithm which leverages real-time measurements to dynamically optimize the 3D position of the drone as well as a cell selection bias tuned for optimized load management. We compare the performance with three benchmark scenarios: i) no drone; ii) a drone positioned above the failing site; and iii) a drone with a statically optimized position and cell selection bias. The results demonstrate that the proposed algorithm significantly improves the call success rate and achieves close to optimal performance.