Building a digital twin for intelligent optical networks [Invited Tutorial]

To support the development of intelligent optical networks, accurate modeling of the physical layer is crucial. Digital twin (DT) modeling, which relies on continuous learning with real-time data, provides a new paradigm to build a virtual replica of the physical layer with a significant improvement...

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Bibliographic Details
Published in:Journal of optical communications and networking 2023-08, Vol.15 (8), p.C242-C262
Main Authors: Zhuge, Qunbi, Liu, Xiaomin, Zhang, Yihao, Cai, Meng, Liu, Yichen, Qiu, Qizhi, Zhong, Xueying, Wu, Jiaping, Gao, Ruoxuan, Yi, Lilin, Hu, Weisheng
Format: Article
Language:English
Subjects:
Analytical models
Data models
Digital twins
Learning
Modelling
Networks
Optical communication
Optical fiber networks
Physical layer
Synchronization
Telemetry
Tutorials
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Summary:To support the development of intelligent optical networks, accurate modeling of the physical layer is crucial. Digital twin (DT) modeling, which relies on continuous learning with real-time data, provides a new paradigm to build a virtual replica of the physical layer with a significant improvement in accuracy and reliability. In addition, DT models will be able to forecast future change by analyzing historical data. In this tutorial, we introduce and discuss three key technologies, including modeling, telemetry, and self-learning, to build a DT for optical networks. The principles and progress of these technologies on major impairments that affect the quality of transmission are presented, and a discussion on the remaining challenges and future research directions is provided.
ISSN:1943-0620
1943-0639
DOI:10.1364/JOCN.483600