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.

中文翻译：

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在光通信中将非线性傅里叶变换与基于神经网络的处理相结合。

我们提出了一种通过在接收器上应用非线性光谱的神经网络后处理来提高基于非线性傅里叶变换（NFT）的光传输系统性能的方法。我们通过数值模型论证了一个数量级的误码率改善，并将此方法与基于接收符号分类的机器学习处理进行了比较。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，它可以找到光通信以外的其他应用。