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.

I. Introduction

With the user terminals of 5G networks grows, more emerging smart devices and internet applications such as augmented reality (AR) /virtual reality (VR), cloud VR/AR, internet of vehicles, ultra-high definition video transmission and so on are being chosen by users [1]. But this will make the user terminals generate a large amount of task data. If the data is transferred to the cloud for processing, this will greatly increase the load of the cloud servers and the latency of users. Mobile edge computing (MEC), an emerging technology in 5G networks, is receiving attentions from more and more people, where servers are placed at the edge of the network and users can offload tasks to MEC servers for processing, thereby significantly reducing energy consumption and delay. For traditional terrestrial networks, placing MEC servers at the ground increases signal attenuation due to multipath effects and blocking caused by non-line-of-sight (NLoS) paths, which severely affects communication quality. Due to the high flexibility and easy deployment of the Unmanned Aerial Vehicle (UAV), UAV-assisted communication system has been widely noticed and studied. The MEC server is integrated with the UAV of the UAV-assisted MEC system, which can greatly reduce the energy consumption and latency of the system since the communication link established between the UAV and the ground user can be considered as a line-of-sight (LoS) path.