UAV-Assisted MEC System Considering UAV Trajectory and Task Offloading Strategy

As an emerging technology, mobile edge computing (MEC) can provide users with higher quality of service (Qos) such as reducing tasks computing latency and energy consumption of user equipments. Unmanned Aerial Vehicle (UAV) -assisted MEC can apply this technology to more scenarios. In this paper, we design a joint optimization algorithm to optimize the user's task offloading strategy and the trajectory of the UAV. When the MEC server interacts with multiple users at the same time, we adopt the differential evolution (DE) algorithm to obtain the offloading policy of each user in the current time slot based on the user location and UAV location. Aiming at the trajectory optimization problem of the UAV, we adopt the optimistic actor-critic (OAC) algorithm, which can minimize the weighted sum of energy consumption and delay of the system, and derive the optimal path through training. Simulation results show that the proposed algorithm is superior to other algorithms in terms of energy consumption and convergence performance.