Digital Twin-Assisted Deep Reinforcement Learning for Joint Caching and Power Allocation in Vehicular Networks

Guobin Zhang, Junran Su, Canxuan Zhong, Feng Ke, Yuling Liu

In recent years, digital twin technology has demonstrated remarkable potential in intelligent transportation systems, leveraging its capabilities of high-precision virtual mapping and real-time dynamic simulation of physical entities. By integrating multi-source data, it constructs virtual replicas of vehicles, roads, and infrastructure, enabling in-depth analysis and optimal decision-making for traffic scenarios. In vehicular networks, existing information caching and transmission systems suffer from low real-time information update and serious transmission delay accumulation due to outdated storage mechanism and insufficient interference coordination, thus leading to a high age of information (AoI). In response to this issue, we focus on pairwise road side unit (RSU) collaboration and propose a digital twin-integrated framework to jointly optimize information caching and communication power allocation. We model the tradeoff between information freshness and resource utilization to formulate an AoI-minimization problem with energy consumption and communication rate constraints, which is solved through deep reinforcement learning within digital twin systems. Simulation results show that our approach reduces the AoI by more than 12 percent compared with baseline methods, validating its effectiveness in balancing information freshness and communication efficiency.