Digital Twin-Enabled Multi-Service Task Offloading in Vehicular Edge Computing Using Soft Actor-Critic

With the rapid development of vehicular networks, the computational capabilities and application scenarios of vehicles are becoming increasingly diverse, leading to a continuous emergence of complex computational tasks. Facing these tasks, a single vehicle node often struggles to handle them effectively; thus, it is necessary to offload tasks to other vehicles with computational resources through Vehicle-to-Vehicle (V2V) communication. However, due to the mobility of vehicles and the limitations of computing and communication resources, efficiently completing these complex computational tasks presents a significant challenge. To address this, this paper proposes an innovative optimization scheme that combines Digital Twin (DT) technology with vehicular edge computing. It constructs digital twins of vehicles through Roadside Units (RSUs) and utilizes these digital twins to optimize task offloading strategies. The scheme aims to jointly optimize transmission power, task offloading ratios, and computational resource allocation to minimize the impact of communication constraints and vehicle mobility on task completion delay. The paper models the wireless communication channel between vehicles using the Nakagami-m fading model, taking into account both transmission delay and computation delay in the overall task completion time. To solve this non-convex optimization problem, we introduce a joiSACnt optimization framework based on the Soft Actor-Critic (SAC) algorithm for efficient task allocation and dynamic transmission power adjustment. The simulation results show that the proposed scheme significantly reduces the maximum task delay and improves overall communication efficiency, particularly when compared with baseline schemes without power optimization and digital twin modules, as well as the DQN and DDPG algorithms. It demonstrates better task processing efficiency and communication performance, providing an effective solution for task handling in vehicular networks.