Journal
OPTICS LETTERS
Volume 45, Issue 13, Pages 3462-3465Publisher
OPTICAL SOC AMER
DOI: 10.1364/OL.394115
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Funding
- H2020 Marie Sklodowska-Curie Actions [GA-2015-713694, 751561]
- Engineering and Physical Sciences Research Council [TRANSNET EP/R035342/1]
- Leverhulme Trust [RP-2018-063]
- Marie Curie Actions (MSCA) [751561] Funding Source: Marie Curie Actions (MSCA)
- EPSRC [EP/R035342/1] Funding Source: UKRI
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We propose a method to improve the performance of the nonlinear Fourier transform (NFT)-based optical transmission system by applying the neural network post-processing of the nonlinear spectrum at the receiver. We demonstrate through numerical modeling about one order of magnitude bit error rate improvement and compare this method with machine learning processing based on the classification of the received symbols. The proposed approach also offers a way to improve numerical accuracy of the inverse NFT; therefore, it can find a range of applications beyond optical communications. Published by The Optical Society under the terms of the Creative Commons Attribution 4.0 License.
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