Deep Neural Network-enabled Fast and Large-Scale QoT Estimation for Dynamic C+L-Band Mesh Networks

Yao Zhang; Yuchen Song; Yan Shi; Jin Li; Chuanbiao Zhang; Yu Tang; Min Zhang; Danshi Wang

doi:10.1364/OFC.2023.Tu2F.4

Optical Fiber Communication Conference (OFC) 2023
Technical Digest Series (Optica Publishing Group, 2023),
paper Tu2F.4
•https://doi.org/10.1364/OFC.2023.Tu2F.4

Deep Neural Network-enabled Fast and Large-Scale QoT Estimation for Dynamic C+L-Band Mesh Networks

Yao Zhang, Yuchen Song, Yan Shi, Jin Li, Chuanbiao Zhang, Yu Tang, Min Zhang, and Danshi Wang

Optical Fiber Communication Conference 2023

San Diego California United States
5–9 March 2023
ISBN: 978-1-957171-18-0

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AI and Optimization in (Disaggregated) Networks (Tu2F)

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Y. Zhang, Y. Song, Y. Shi, J. Li, C. Zhang, Y. Tang, M. Zhang, and D. Wang, "Deep Neural Network-enabled Fast and Large-Scale QoT Estimation for Dynamic C+L-Band Mesh Networks," in Optical Fiber Communication Conference (OFC) 2023, Technical Digest Series (Optica Publishing Group, 2023), paper Tu2F.4.

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Abstract

A fast and accurate QoT estimation scheme using deep neural network is proposed for dynamic C+L-band large-scale mesh networks. The calculation time is decreased dramatically and it is applied to measure the fiber broken case.

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