Data-driven optimization of drone-assisted cellular networks

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.