Toward Efficient Deployment and Synchronization in Digital Twins-Empowered Networks
This work addresses a domain-specific problem for wireless network operators by providing incremental improvements in digital twin management.
The paper tackles the challenge of efficient deployment and synchronization of digital twins in dynamic multi-access edge computing environments by jointly optimizing placement and update scheduling, achieving lower latency, enhanced information freshness, and reduced system cost compared to benchmarks.
Digital twins (DTs) are envisioned as a key enabler of the cyber-physical continuum in future wireless networks. However, efficient deployment and synchronization of DTs in dynamic multi-access edge computing (MEC) environments remains challenging due to time-varying communication and computational resources. This paper investigates the joint optimization of DT deployment and synchronization in dynamic MEC environments. A deep reinforcement learning (DRL) framework is proposed for adaptive DT placement and association to minimize interaction latency between physical and digital entities. To ensure semantic freshness, an update scheduling policy is further designed to minimize the long-term weighted sum of the Age of Changed Information (AoCI) and the update cost. A relative policy iteration algorithm with a threshold-based structure is developed to derive the optimal policy. Simulation results show that the proposed methods achieve lower latency, enhanced information freshness, and reduced system cost compared with benchmark schemes