RIS-Enhanced MEC Framework for Vehicular Networks: Minimizing Delay With Deep Reinforcement Learning

Mobile edge computing (MEC) allows ground vehicles (GVs) to offload computationally intensive tasks to edge servers, offering advantages over centralized cloud computing by reducing the energy consumption and network congestion. However, the dynamic nature of communication links often results in suboptimal offloading performance due to signal occlusion and interference. Reconfigurable intelligent surface (RIS) technology, a promising component of sixth-generation (6G) communication networks, can enhance wireless network capabilities by modifying the phase and amplitude of reflective components. In this paper, we propose a RIS-assisted MEC strategy to provide a distributed edge intelligence (DEI) solution for systems providing fast wireless connectivity and low latency to ground vehicles in dynamic environments. The RIS-assisted vehicular networks model has recently showed promising results when the delay was minimized by jointly optimizing the computation offloading strategy and the RIS phase shift. The delay optimization problem was modeled as a markov decision process (MDP), and a delay minimization algorithm (DDPG-DM) based on deep reinforcement learning (DRL) was proposed. Simulation results demonstrate that the proposed algorithm significantly outperforms existing non-RIS learning algorithms and classical methods, achieving superior performance in reducing delay. The findings suggest that integrating RIS with MEC can substantially improve the efficiency of computation offloading in dynamic vehicular environments.