A recent publication by TRANSNET members from Aston University, published in Optics Letters, examines neural network-based processing.
In this paper the authors 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. Unlike similar works, the artificial neural network reduces the impact of noise through regression rather than improve the decision boundaries via classification.
The full article can be found on the journal's site.
Oleksandr Kotlyar, Maryna Pankratova, Morteza Kamalian-Kopae, Anastasiia Vasylchenkova, Jaroslaw Prilepsky, and Sergei Turitsyn, "Combining nonlinear Fourier transform and neural network-based processing", Optics Letters., Vol 45, Issue no. 13, pp. 3462-3465, May, 2020.