Toward Efficient Deployment and Synchronization in Digital Twins-Empowered Networks

TL;DR

This paper proposes a DRL-based framework for optimizing deployment and synchronization of digital twins in dynamic MEC environments, reducing latency and improving information freshness.

Contribution

It introduces a joint optimization approach for DT placement and synchronization using deep reinforcement learning and a novel update scheduling policy.

Findings

Achieves lower latency compared to benchmark schemes.

Enhances information freshness through optimized update scheduling.

Reduces system cost while maintaining semantic freshness.

Abstract

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