Data-Driven Positioning of Drone Base Stations in Emergency Scenarios

Cellular networks are carefully planned to provide sufficient coverage and capacity under normal circumstances. However, in emergency scenarios like floodings, wildfires, earthquakes or even terrorist attacks, part of the network may no longer be operational while at the same time a traffic hotspot may occur as a consequence of such an event. In such scenarios it is of utmost importance to quickly restore wireless coverage with sufficient capacity. To achieve this goal, we consider the dynamic deployment of drone-mounted base stations and propose a data-driven algorithm which optimizes the positions of the drone base stations based on measurement values that are readily available. We demonstrate that the use of well-positioned drones yields significant performance improvements and that the proposed method outperforms relevant benchmarks and is able to achieve close to optimal performance. For example, in rural scenarios we may observe in certain cases a twofold reduction in the fraction of failed calls compared to a pre-planned deployment of drones, and an even greater improvement over a setting without drones.