Reconfigurable Intelligent Surface Assisted VEC Based on Multi-Agent Reinforcement Learning

TL;DR

This paper introduces a novel deep reinforcement learning framework for optimizing reconfigurable intelligent surface-assisted vehicular edge computing, improving communication reliability and task offloading efficiency amid obstacles.

Contribution

It proposes a multi-agent DRL approach combined with BCD for joint optimization of power and phase-shift in RIS-assisted VEC systems, addressing non-convex challenges.

Findings

Outperforms centralized DDPG and random schemes in simulations.

Enhances communication robustness in obstacle-prone environments.

Improves task offloading success rate and system performance.

Abstract

Vehicular edge computing (VEC) is an emerging technology that enables vehicles to perform high-intensity tasks by executing tasks locally or offloading them to nearby edge devices. However, obstacles such as buildings may degrade the communications and incur communication interruptions, and thus the vehicle may not meet the requirement for task offloading. Reconfigurable intelligent surfaces (RIS) is introduced to support vehicle communication and provide an alternative communication path. The system performance can be improved by flexibly adjusting the phase-shift of the RIS. For RIS-assisted VEC system where tasks arrive randomly, we design a control scheme that considers offloading power, local power allocation and phase-shift optimization. To solve this non-convex problem, we propose a new deep reinforcement learning (DRL) framework that employs modified multi-agent deep deterministic policy gradient (MADDPG) approach to optimize the power allocation for vehicle users (VUs) and block coordinate descent (BCD) algorithm to optimize the phase-shift of the RIS. Simulation results show that our proposed scheme outperforms the centralized deep deterministic policy gradient (DDPG) scheme and random scheme.