Lifecycle Management of Optical Networks with Dynamic-Updating Digital Twin: A Hybrid Data-Driven and Physics-Informed Approach

2025-04-28

IEEE Journal on Selected Areas in Communications

AI Summary

To address the challenge of real-time synchronization between digital twins (DTs) and physical optical networks—limiting dynamic service adaptability throughout the network lifecycle—this paper proposes a dynamically updated DT framework for fiber channel performance prediction. Methodologically, it introduces the first DT dynamic update mechanism for optical networks, integrating physics-informed neural networks (PINNs), partial differential equation (PDE)-constrained hybrid modeling, real-time parameter identification, and a closed-loop feedback architecture. This enables synchronous, adaptive updates of multi-physical parameters—including Raman gain, amplifier frequency response, and connection loss—across C- and L-bands. Experimental results demonstrate a 100× speedup in prediction over conventional numerical methods; a 1.4 dB reduction in performance estimation error following device replacement; and validation of high accuracy (sub-dB), low latency (millisecond-level), and physical consistency in both large-scale simulations and live C+L-band field trials.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

Abstract

Digital twin (DT) techniques have been proposed for the autonomous operation and lifecycle management of next-generation optical networks. To fully utilize potential capacity and accommodate dynamic services, the DT must dynamically update in sync with deployed optical networks throughout their lifecycle, ensuring low-margin operation. This paper proposes a dynamic-updating DT for the lifecycle management of optical networks, employing a hybrid approach that integrates data-driven and physics-informed techniques for fiber channel modeling. This integration ensures both rapid calculation speed and high physics consistency in optical performance prediction while enabling the dynamic updating of critical physical parameters for DT. The lifecycle management of optical networks, covering accurate performance prediction at the network deployment and dynamic updating during network operation, is demonstrated through simulation in a large-scale network. Up to 100 times speedup in prediction is observed compared to classical numerical methods. In addition, the fiber Raman gain strength, amplifier frequency-dependent gain profile, and connector loss between fiber and amplifier on C and L bands can be simultaneously updated. Moreover, the dynamic-updating DT is verified on a field-trial C+L-band transmission link, achieving a maximum accuracy improvement of 1.4 dB for performance estimation post-device replacement. Overall, the dynamic-updating DT holds promise for driving the next-generation optical networks towards lifecycle autonomous management.

Problem

• Dynamic-updating digital twin for lifecycle optical network management
• Hybrid data-driven and physics-informed fiber channel modeling
• Enhancing prediction speed and accuracy in optical performance

Innovation

• Dynamic-updating digital twin for lifecycle management
• Hybrid data-driven and physics-informed modeling
• 100x speedup in performance prediction