Intelligent Energy-Efficient Resource Allocation for Multi-UAV-Assisted Mobile Edge Computing Networks

The unmanned aerial vehicle (UAV)assisted mobile edge computing (MEC) has been deemed a promising solution for energy-constrained devices to run smart applications with computation-intensive and latency-sensitive requirements, especially in some infrastructure-limited areas or some emergency scenarios. However, the multi-UAVassisted MEC network remains largely unexplored. In this paper, the dynamic trajectory optimization and computation offloading are studied in a multi-UAVassisted MEC system where multiple UAVs fly over a target area with different trajectories to serve ground users. By considering the dynamic channel condition and random task arrival and jointly optimizing UAVs' trajectories, user association, and subchannel assignment, the average long-term sum of the user energy consumption minimization problem is formulated. To address the problem involving both discrete and continuous variables, a hybrid decision deep reinforcement learning (DRL)-based intelligent energy-efficient resource allocation and trajectory optimization algorithm is proposed, named HDRT algorithm, where deep Q network (DQN) and deep deterministic policy gradient (DDPG) are invoked to process discrete and continuous variables, respectively. Simulation results show that the proposed HDRT algorithm converges fast and outperforms other benchmarks in the aspect of user energy consumption and latency.