Intelligent Reflecting Surface and Network Slicing Assisted Vehicle Digital Twin Update

Vehicle digital twin (VDT) can support multiple different vehicle services through updating, and the updating of VDT faces two fundamental issues: one is the isolation of VDT, that is VDTs can run various services without being affected by others; the other one is the timeliness of VDT updates for low latency services. In this paper, we first propose to ensure the isolation of VDTs with the assistance of intelligent reflecting surface (IRS) and network slicing (NS), and obtain better update time of the VDTs within limited resources. Specifically, we divide resources for VDTs with different update requirements to ensure the isolation of VDTs and the resources required for update. On the other hand, considering that the communication performance between vehicles and base stations is affected by urban building density, we propose using an intelligent controller to achieve intelligent control of the physical channel by adjusting the phase shift of passive reflective elements to ensure better transmission performance during VDTs updating. Secondly, considering the dynamic variability of vehicles and the environment, we propose an improved deep reinforcement learning algorithm based on the actor-critic framework to allocate communication, computing resources, and adjust the phase shift of the IRS. Finally, a large number of simulation results indicate that our proposed algorithm performs better than the benchmark algorithms.