Predicting nonlinear reshaping of periodic signals in optical fibre with a neural network

Sonia Boscolo; John M. Dudley; Christophe Finot

doi:10.1016/j.optcom.2023.129563

Predicting nonlinear reshaping of periodic signals in optical fibre with a neural network

Sonia Boscolo
, John M. Dudley
, Christophe Finot^*

^*Corresponding author for this work

Institut FEMTO-ST, Université Bourgogne Franche-Comté CNRS UMR 6174, Besançon, 25000, France
Laboratoire Interdisciplinaire Carnot de Bourgogne, UMR 6303 CNRS-Université de Bourgogne-Franche-Comté, 9 avenue Alain Savary, BP 47870, 21078 Dijon Cedex, France

Research output: Contribution to journal › Article › peer-review

13 Citations (Scopus)

7 Downloads (Pure)

Abstract

We deploy a supervised machine-learning model based on a neural network to predict the temporal and spectral reshaping of a simple sinusoidal modulation into a pulse train having a comb structure in the frequency domain, which occurs upon nonlinear propagation in an optical fibre. Both normal and anomalous second-order dispersion regimes of the fibre are studied, and the speed of the neural network is leveraged to probe the space of input parameters for the generation of custom combs or the occurrence of significant temporal or spectral focusing.

Original language	English
Article number	129563
Journal	Optics Communications
Volume	542
Early online date	5 May 2023
DOIs	https://doi.org/10.1016/j.optcom.2023.129563 https://doi.org/10.1016/j.optcom.2023.129563
Publication status	Published - 1 Sept 2023

Bibliographical note

Keywords

Neural networks
Nonlinear propagation
Optical fibres
Pulse shaping

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OC - Finot - 2023_vf_SB
Copyright © 2023. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
Accepted author manuscript, 1.18 MBLicence: CC BY-NC-ND 4.0

Cite this

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abstract = "We deploy a supervised machine-learning model based on a neural network to predict the temporal and spectral reshaping of a simple sinusoidal modulation into a pulse train having a comb structure in the frequency domain, which occurs upon nonlinear propagation in an optical fibre. Both normal and anomalous second-order dispersion regimes of the fibre are studied, and the speed of the neural network is leveraged to probe the space of input parameters for the generation of custom combs or the occurrence of significant temporal or spectral focusing.",

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N2 - We deploy a supervised machine-learning model based on a neural network to predict the temporal and spectral reshaping of a simple sinusoidal modulation into a pulse train having a comb structure in the frequency domain, which occurs upon nonlinear propagation in an optical fibre. Both normal and anomalous second-order dispersion regimes of the fibre are studied, and the speed of the neural network is leveraged to probe the space of input parameters for the generation of custom combs or the occurrence of significant temporal or spectral focusing.

AB - We deploy a supervised machine-learning model based on a neural network to predict the temporal and spectral reshaping of a simple sinusoidal modulation into a pulse train having a comb structure in the frequency domain, which occurs upon nonlinear propagation in an optical fibre. Both normal and anomalous second-order dispersion regimes of the fibre are studied, and the speed of the neural network is leveraged to probe the space of input parameters for the generation of custom combs or the occurrence of significant temporal or spectral focusing.

KW - Neural networks

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KW - Pulse shaping

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M1 - 129563

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Predicting nonlinear reshaping of periodic signals in optical fibre with a neural network

Abstract

Bibliographical note

Keywords

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