关键词: 无人机, 移动边缘计算, 设备到设备, 深度强化学习, 计算卸载, 延时最小化

Abstract: In traditional mobile edge computing systems, users face issues such as communication channel interruptions caused by dense obstacles and terrain structures, and under-utilization of idle system resources.These issues make it challenging to complete intensive computing tasks with low delay and power consumption.To address this, a UAV(Unmanned Aerial Vehicle)-assisted terminal pass-through collaborative mobile edge computing system is established. In this system, the computing user offloads part of the task to the idle user by establishing a direct connection link on the ground. The idle user uses their computing resources to assist with the calculation while offloading the remaining tasks to the UAV configured with a mobile edge computing server.A mathematical model of this new system is established, and a computational offloading strategy based on the deep deterministic policy gradient algorithm is proposed. This strategy optimizes the dual offloading rate and the maneuverability of the UAV to minimize the processing delay of computing tasks, under the constraints of UAV power and user movement range. Simulation tests in a simulated continuous state space environment show that the proposed offloading computing strategy optimization scheme can efficiently use resources in the collaborative network and effectively reduce task processing delay compared to other baseline algorithms.

Key words: unmanned aerial vehicle(UAV), mobile edge computing, device-to-device communication(D2D), deep reinforcement learning, computation offloading, minimization of delay