Abstract: In order to compensate the performance loss caused by obstacle blocking in mobile edge computing (MEC) system in 6G-enabled “intelligent Internet of Things”,this paper proposes a partial task offloading scheme supported by aerial reconfigurable intelligent surface (RIS).Firstly,we investigate the joint design of the RIS phase shift vector,the proportion of offloading task,time slot allocation,the transmit power of users and the position of UAV,formulating a non-convex problem for minimization of the total energy consumption of users.Then,the original non-convex problem is decomposed into four subproblems,and the proximal policy optimization (PPO) method in deep reinforcement learning (DRL) is utilized to provide time slot allocation.The alternative optimization (AO) is leveraged to decouple the original problem into four subproblems,including the RIS phase shift design,the convex optimization of transmit power and offloading task amount,and the UAV altitude optimization.Simulation results show that the proposed PPO model can be trained quickly,the total energy consumption of users can be reduced by about 23% and 5.3%,compared with the fully-offload strategy and fixed-UAV-height strategy,respectively.

Key words: Deep reinforcement learning, Mobile edge computing, Reconfigurable intelligent surface, Task offloading, Unmanned aerial vehicle