Delay-Aware Digital Twin Synchronization in Mobile Edge Networks with Semantic Communications

2025-03-06

AI Summary

To address high synchronization latency in digital twin (DT) systems over mobile edge networks—caused by limited channel capacity—this paper pioneers the integration of semantic communication into the DT synchronization framework, jointly optimizing user equipment’s synchronization policy, transmit power, and local/base-station computational resource allocation. To tackle time-varying channels and sequential decision-making challenges induced by user mobility, we propose a soft actor-critic (SAC)-based deep reinforcement learning algorithm. The method achieves significant transmission efficiency gains while guaranteeing semantic fidelity, reducing average synchronization latency by 13.2% compared to conventional communication and static resource allocation baselines. Key contributions include: (i) the first deep integration of semantic communication with DT synchronization; (ii) a dynamic-environment-aware joint resource optimization paradigm; and (iii) a scalable, SAC-driven decision-making framework for real-time DT coordination.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Multiagent Systems: Agent Communication

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

The synchronization of digital twins (DT) serves as the cornerstone for effective operation of the DT framework. However, the limitations of channel capacity can greatly affect the data transmission efficiency of wireless communication. Unlike traditional communication methods, semantic communication transmits the intended meanings of physical objects instead of raw data, effectively saving bandwidth resource and reducing DT synchronization latency. Hence, we are committed to integrating semantic communication into the DT synchronization framework within the mobile edge computing system, aiming to enhance the DT synchronization efficiency of user devices (UDs). Our goal is to minimize the average DT synchronization latency of all UDs by jointly optimizing the synchronization strategy, transmission power of UDs, and computational resource allocation for both UDs and base station. The formulated problem involves sequential decision-making across multiple coherent time slots. Furthermore, the mobility of UDs introduces uncertainties into the decision-making process. To solve this challenging optimization problem efficiently, we propose a soft actor-critic-based deep reinforcement learning algorithm to optimize synchronization strategy and resource allocation. Numerical results demonstrate that our proposed algorithm can reduce synchronization latency by up to 13.2% and improve synchronization efficiency compared to other benchmark schemes.

Problem

• Enhance digital twin synchronization efficiency in mobile edge networks.
• Minimize synchronization latency by optimizing strategy and resource allocation.
• Address uncertainties in decision-making due to user device mobility.

Innovation

• Integrates semantic communication for DT synchronization
• Optimizes synchronization strategy and resource allocation
• Uses deep reinforcement learning for latency reduction