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

Abstract

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.

Polarization control parameters evolution of genetic algorithm‐based 2 µm Tm‐doped fiber laser

“…Recently, Lilin Yi research team construct DT models to forecast future change by analyzing historical data. 20 They also present a recurrent neural network that is proposed to realize fast and accurate femtosecond modelocked fiber laser modeling. 21 In previous work, we successfully achieved stable noiselike mode-locked pulse output using an adaptive GA in TDFL.…”