SA-DTS: Semantic-Aware Digital Twin Synchronization over 6G Networks

Abstract

Digital Twins (DTs) are emerging as a cornerstone of the 6G vision, enabling real-time cyber-physical mirroring for smart manufacturing, autonomous vehicles, and remote healthcare. However, maintaining high-fidelity synchronization at scale demands an enormous and sustained uplink bandwidth, threatening both the feasibility and the energy efficiency of large deployments. We propose a Semantic-Aware DT Synchronization (SA-DTS) framework that radically redefines the synchronization pipeline: instead of streaming raw sensor or video data, a lightweight neural semantic encoder at the physical-world source extracts only task-relevant features and transmits compact semantic descriptors over the 6G air interface. At the DT replica, a paired decoder coupled with a dynamic Knowledge Graph (KG) reconstructs the full contextual state. A hierarchical KG partitioning strategy with an adaptive partition count $G = \lceil N / \log_2 N \rceil$ ensures that aggregate update overhead scales as $O(N \log N)$ rather than $O(N^2)$, making the framework viable for deployments with hundreds of simultaneously twinned entities. Extensive simulations on three canonical DT workloads -- industrial robot control, patient-monitoring, and vehicular platooning -- demonstrate bandwidth savings of up to 94%, end-to-end synchronization latency reductions of 87%, and KG-assisted state-reconstruction accuracy exceeding 97%, all under realistic 6G channel conditions. Empirical correlation confirms that the proposed Semantic Fidelity Score tracks standard task metrics (collision accuracy, alarm F1, spacing deviation) with Pearson $r > 0.97$ (95% CI: [0.961, 0.982]). Our results reveal that semantic communication is not merely a compression tool but a fundamental enabler for truly real-time, scalable DT ecosystems.