Joint Vehicular Social Semantic Extraction, Transmission and Cache for High QoE Digital Twin

Digital twins have been extensively explored in vehicular social networks, while wireless communication quality is limited to support digital twins and their applications due to the high mobility of the vehicular environment. To address this issue, we propose a semantic communication empowered two-level digital twin architecture, called Semantic Twin, which supports reliable communication of efficient cloud-edge collaborative vehicular digital twin, specifically comprising low-level semantic twin (L-SemTwin) and high-level semantic twin (H-SemTwin). First, we design a social behavior semantic extraction scheme based on semantic encoder on the vehicle side to capture essential content features. Second, a semantic transmission scheme in vehicle-to-everything communication is performed to reduce the overall transmission burden and error rate, and build L-SemTwin. Moreover, we propose a deep reinforcement learning-based semantic caching strategy with the assistance of city-wise semantic information in cloud-side H-SemTwin. The experiment results demonstrate the promotion under the proposed architecture compared to conventional methods in terms of the quality of communication and user experience in vehicular social networks.