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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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Published in: | Journal of optical communications and networking 2023-08, Vol.15 (8), p.C242-C262 |
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container_end_page | C262 |
container_issue | 8 |
container_start_page | C242 |
container_title | Journal of optical communications and networking |
container_volume | 15 |
creator | Zhuge, Qunbi Liu, Xiaomin Zhang, Yihao Cai, Meng Liu, Yichen Qiu, Qizhi Zhong, Xueying Wu, Jiaping Gao, Ruoxuan Yi, Lilin Hu, Weisheng |
description | 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. |
doi_str_mv | 10.1364/JOCN.483600 |
format | article |
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source | IEEE Electronic Library (IEL) Journals |
subjects | Analytical models Data models Digital twins Learning Modelling Networks Optical communication Optical fiber networks Physical layer Synchronization Telemetry Tutorials |
title | Building a digital twin for intelligent optical networks [Invited Tutorial] |
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