Generalization Properties of Machine Learning-based Raman Models

U. C. de Moura; D. Zibar; A. M. Rosa Brusin; A. Carena; F. Da Ros

doi:10.1364/OFC.2021.Th1A.28

Optical Fiber Communication Conference (OFC) 2021
OSA Technical Digest (Optica Publishing Group, 2021),
paper Th1A.28
•https://doi.org/10.1364/OFC.2021.Th1A.28

Generalization Properties of Machine Learning-based Raman Models

U. C. de Moura, D. Zibar, A. M. Rosa Brusin, A. Carena, and F. Da Ros

Optical Fiber Communication Conference 2021

Washington, DC United States
6–11 June 2021
ISBN: 978-1-943580-86-6

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Joint Poster Session 2 (Th1A)

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U. C. de Moura, D. Zibar, A. M. Rosa Brusin, A. Carena, and F. Da Ros, "Generalization Properties of Machine Learning-based Raman Models," in Optical Fiber Communication Conference (OFC) 2021, P. Dong, J. Kani, C. Xie, R. Casellas, C. Cole, and M. Li, eds., OSA Technical Digest (Optica Publishing Group, 2021), paper Th1A.28.

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Abstract

We investigate the generalization capabilities of neural network–based Ra-man amplifier models. The new proposed model architecture, including fiber parameters as inputs, can predict Raman gains of fiber types unseen during training, unlike previous fiber–specific models.

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