Improved DRL-based energy-efficient UAV control for maximum lifecycle

Abstract

Unmanned aerial vehicles (UAVs) operating as airborne base stations (UAV-BSs) provide efficient on-demand services to ground users. UAV-BSs are inherently flexible and mobile, allowing them to be strategically deployed based on ground user distribution and quality of service requirements, including coverage rate, system lifecycle, and user fairness. Owing to the limited battery capacity and coverage range of the UAVs, managing them to extend their operational lifecycle, ensure service fairness, and maintain a specific real-time coverage rate is challenging. Therefore, a multi-objective optimization problem with constrained Pareto dominance is formulated. Subsequently, a novel assisted deep reinforcement learning model is developed to maximize the minimum remaining energy while simultaneously considering user fairness and coverage-rate requirements. The particle swarm optimization algorithm is adopted to assist multi-agent cooperative deep reinforcement learning. Finally, the simulation results show that the proposed model outperforms the other popular methods in terms of user fairness, system lifecycle, coverage rate, and energy efficiency in the context of multi-objective, multi-agent cooperative coverage control deployment.