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

TL;DR

This work tackles DT synchronization in MEC under mobility by leveraging semantic communication to reduce transmitted data. It formulates a time-slotted system where UDs sense, semantically extract, and offload information to an edge server, and then recover semantic data at the edge, all while meeting latency and energy constraints. An SAC-based DRL algorithm optimizes the semantic extraction factor, UD transmission power, and edge/UD computational allocations within a joint MDP framework, accounting for mobility-induced uncertainty. Empirical results show the proposed SAC approach yields faster convergence and lower average synchronization latency than benchmarks, achieving up to 13.2% improvement, thereby enhancing DT synchronization efficiency in bandwidth-limited mobile networks.

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.

Figures (7)

• Figure 1: DT synchronization framework based on semantic communications.
• Figure 2: Training workflow of the SAC algorithm.
• Figure 3: The convergence performance comparison.
• Figure 4: DT synchronization latency versus the number of UDs.
• Figure 5: DT synchronization latency versus the size of required sensed data.