Optimizing Drone Energy Use for Emergency Communications in Disasters via Deep Reinforcement Learning

Abstract

For a communication control system in a disaster area where drones (also called unmanned aerial vehicles (UAVs)) are used as aerial base stations (ABSs), the reliability of communication is a key challenge for drones to provide emergency communication services. However, the effective configuration of UAVs remains a major challenge due to limitations in their communication range and energy capacity. In addition, the relatively high cost of drones and the issue of mutual communication interference make it impractical to deploy an unlimited number of drones in a given area. To maximize the communication services provided by a limited number of drones to the ground user equipment (UE) within a certain time frame while minimizing the drone energy consumption, we propose a multi-agent proximal policy optimization (MAPPO) algorithm. Considering the dynamic nature of the environment, we analyze diverse observation data structures and design novel objective functions to enhance the drone performance. We find that, when drone energy consumption is used as a penalty term in the objective function, the drones—acting as agents—can identify the optimal trajectory that maximizes the UE coverage while minimizing the energy consumption. At the same time, the experimental results reveal that, without considering the machine computing power required for training and convergence time, the proposed key algorithm demonstrates better performance in communication coverage and energy saving as compared with other methods. The average coverage performance is 10–45% higher than that of the other three methods, and it can save up to 3% more energy.