@inproceedings{1783a6d709e0469f9c3eccdf5ca46e3f,
title = "Fast and accurate nonlinear interference in-band spectrum prediction for sparse channel allocation",
abstract = "We propose and numerically evaluate a machine-learning-based nonlinear interference spectrum estimator for a coherent optical network. The solution shows a root-mean-squared error of about 0.13 dB compared with split-step Fourier simulation when estimating the nonlinear interference variance.",
keywords = "fiber nonlinearity, machine learning, nonlinear interference power spectral density, optical network",
author = "Isaia Andrenacci and Matteo Lonardi and Petros Ramantanis and Elie Awwad and Ekhine Irurozki and Stephan Clemencon",
note = "Publisher Copyright: {\textcopyright} 2023 IFIP.; 2023 International Conference on Optical Network Design and Modeling, ONDM 2023 ; Conference date: 08-05-2023 Through 11-05-2023",
year = "2023",
month = jan,
day = "1",
language = "English",
series = "Proceedings of the 2023 International Conference on Optical Network Design and Modeling, ONDM 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Teresa Gomes and David Larrabeiti-Lopez and Carmen Mas-Machuca and Luca Valcarenghi and Luisa Jorge and Paulo Melo",
booktitle = "Proceedings of the 2023 International Conference on Optical Network Design and Modeling, ONDM 2023",
}