@inproceedings{f4ab4f8f5de04c5b92dcccdb23e342e1,
title = "Neural Networks for Nonlinear Fourier Spectrum Computation",
abstract = "We demonstrate that neural networks can outperform conventional numerical nonlinear Fourier transform algorithms for processing the noise-corrupted optical signal. Applying the Bayesian hyper-parameters optimisation, we design the architecture of neural networks capable to compute nonlinear signal spectrum at low SNR more accurately than conventional algorithms.",
author = "Egor Sedov and Freire, {Pedro J.} and Igor Chekhovskoy and Sergei Turitsyn and Jaroslaw Prilepsky",
note = "Funding Information: ES and ST are supported by the EPSRC programme grant TRANSNET, EP/R035342/1. JP and ST acknowledge the support of Leverhulme Trust project RPG-2018-063. ES acknowledges the support from the Russian Science Foundation under Grant 17-72-30006, IC acknowledges the grant of the President of the Russian Federation (MK-677.2020.9). Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 European Conference on Optical Communication, ECOC 2021 ; Conference date: 13-09-2021 Through 16-09-2021",
year = "2021",
doi = "10.1109/ECOC52684.2021.9605844",
language = "English",
series = "2021 European Conference on Optical Communication, ECOC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 European Conference on Optical Communication, ECOC 2021",
address = "United States",
}