Archivio Istituzionale della RicercaNon-linearity buildup in dispersion-managed network sections can be severe due to the small accumulated dispersion causing non-Gaussian signal distribution and correlation between noise contributions. We propose a machine learning-assisted, spatially disaggregated model able to provide a real-time and accurate non-linearity estimation in this scenario.
Combining Machine Learning and the GN Model for Fast NLI Prediction in Dispersion-Managed Links / Virgillito, E., Ietro, R., Napoli, A., Bhyri, S.K., Galimberti, G., Wakim, W., Curri, V.. - (2025), pp. 1-4. (2025 European Conference on Optical Communications, ECOC 2025 Copenhagen (Den) 28 September 2025 - 02 October 2025) [10.1109/ecoc66593.2025.11263355].
Combining Machine Learning and the GN Model for Fast NLI Prediction in Dispersion-Managed Links
Virgillito, Emanuele;Ietro, Rosario;Curri, Vittorio
2025
Abstract
Non-linearity buildup in dispersion-managed network sections can be severe due to the small accumulated dispersion causing non-Gaussian signal distribution and correlation between noise contributions. We propose a machine learning-assisted, spatially disaggregated model able to provide a real-time and accurate non-linearity estimation in this scenario.
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Combining_Machine_Learning_and_the_GN_Model_for_Fast_NLI_Prediction_in_Dispersion-Managed_Links.pdf
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ECOC_2025__XCI.pdf
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https://hdl.handle.net/11583/3015478