Prediction of Second-Order Moments of Inter-Channel Interference with Principal Component Analysis and Neural Networks

Rasmus Thomas Jones, Júlio César Medeiros Diniz, Metodi Plamenov Yankov, Molly Piels, Andy Doberstein, Darko Zibar

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Abstract

A machine learning framework for predicting auto-correlation functions of inter-channel nonlinearities within the uncompensated optical fiber link is proposed. Low generalization error is obtained on the test data.
Original languageEnglish
Title of host publicationProceedings of the 43rd European Conference and Exhibition on Optical Communications (ECOC 2017)
Number of pages3
PublisherIEEE
Publication date2017
ISBN (Electronic)978-1-5386-5624-2
DOIs
Publication statusPublished - 2017
Event43rd European Conference and Exhibition on Optical Communications (ECOC 2017) - The Swedish Exhibition & Congress Centre, Gothenburg, Sweden
Duration: 17 Sep 201721 Sep 2017

Conference

Conference43rd European Conference and Exhibition on Optical Communications (ECOC 2017)
LocationThe Swedish Exhibition & Congress Centre
CountrySweden
CityGothenburg
Period17/09/201721/09/2017

Cite this

Jones, R. T., Medeiros Diniz, J. C., Yankov, M. P., Piels, M., Doberstein, A., & Zibar, D. (2017). Prediction of Second-Order Moments of Inter-Channel Interference with Principal Component Analysis and Neural Networks. In Proceedings of the 43rd European Conference and Exhibition on Optical Communications (ECOC 2017) IEEE. https://doi.org/10.1109/ECOC.2017.8346176