Kotlyar Oleksandr, Pankratova Maryna, Kamalian-Kopae Morteza, Vasylchenkova Anastasiia, Prilepsky Jaroslaw E, Turitsyn Sergei K
Opt Lett. 2020 Jul 1;45(13):3462-3465. doi: 10.1364/OL.394115.
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.
我们提出了一种方法，通过在接收器处对非线性频谱进行神经网络后处理，来提高基于非线性傅里叶变换（NFT）的光传输系统的性能。我们通过数值建模证明，误码率提高了约一个数量级，并将该方法与基于接收符号分类的机器学习处理方法进行了比较。所提出的方法还提供了一种提高逆NFT数值精度的方法；因此，它可以在光通信之外找到一系列应用。
