Trajectory Design for Multi-UAV-Enabled Wireless Powered Communication Networks: A Multi-Agent DRL Approach

Unmanned aerial vehicles (UAVs) have emerged as a promising solution to support ground networks. In UAV-enabled wireless powered communication networks (WPCNs), the UAV provides data collection (DC) and energy transmission (ET) services for ground energy harvesting (EH) IoT devices. In view of the limited working efficiency of a single UAV, we propose a multi-UAV-enabled WPCN system. In order to further improve the efficiency of the system, we jointly optimize the data transmission performance of the network and the average harvested energy of IoT devices by designing the trajectories of the UAVs. The high mobility of UAVs causes the rapid change of channels, which leads to limitations of the centralized management scheme. Therefore, the problem is modeled as a multi-agent deep reinforcement learning (MADRL) problem. The scenario of multiple UAVs working collaboratively is formulated as a decentralized partially observable Markov decision process (Dec-POMDP). We develop a multi-agent deep deterministic policy gradient (MADDPG)-based approach to design UAVs' trajectories by adjusting the flying velocity and direction. Simulation results prove that the proposed algorithm far exceeds the benchmark schemes.