Priority-Aware Resource Allocation for RIS-assisted Mobile Edge Computing Networks: A Deep Reinforcement Learning Approach

In this work, we investigate a reconfigurable intelligent surface (RIS) assisted mobile edge computing (MEC) network, where multiple users offload tasks to edge servers (ES) via RIS for accelerating computation. However, in practical MEC networks, computational tasks often exhibit diverse priorities, resulting in varying degrees of computational utility. This variability poses significant challenges to system optimization. In response to this challenge, we propose a deep reinforcement learning (DRL)-based approach to improve the performance of the RIS-assisted MEC network, where the task priority and resource allocations are jointly considered. Specifically, we exploit functions to evaluate the computational benefit of different tasks. Then, we devise a joint resource allocation scheme, which jointly considers RISs’ phase shifts, user-RIS allocation, and task offloading, to maximize the system utility. To solve the problem under the complicated environment of fading channels and various task priorities, we employ the DRL approach to obtain effective resource allocation strategies to improve the system performance. Simulations are finally conducted to validate the effectiveness and superiority of the proposed schemes in this work.