Deep Reinforcement Learning for Communication and Computing Resource Allocation in RIS Aided MEC Networks

Abstract

In this paper, we apply reconfigurable intelligent surface (RIS) technique to aid the computation offloading of mobile edge computing (MEC) network and investigate how it can be exploited to reduce computation offloading delay. In order to minimize the long-term computation offloading delay, we formulate an optimization problem, which jointly optimizes the power control, computation offloading volume, the edge computing resource assigned to each user equipment (UE), as well as the RIS phase shift. To tackle this problem, we first convert it into a Markov decision process (MDP), then propose an efficient algorithm based on deep reinforcement learning (DRL), namely deep deterministic policy gradient (DDPG). Numerical results demonstrate that 1) compared to the MEC network without RIS, the RIS aid MEC network can achieve lower delay; 2) the proposed DDPG-based scheme can learn from the RIS aided environment to effectively reduce the computation offloading delay.