Neural Network-Based Noise Suppression Method for NFT-Based Multi-Eigenvalue Transmission

Abstract

In this letter, we mathematically analyze the influence of the perturbation of the real and imaginary part of the eigenvalues on the amplitude and phase noise of the discrete spectrum based on the nonlinear Fourier transform (NFT). Considering the correlation between the multiple eigenvalues, the impact of the noise is more complex and difficult to be numerically processed. We propose a method to suppress the noise interference by applying neural network (NN) of the nonlinear spectrum domain as an equalizer at the receiver. As shown in simulation results, the proposed method could effectively mitigate the impact of noise on NFT-based multi-eigenvalue transmission. Compared with other method, the proposed method shows a significant performance gain. Our research provides a novel perspective for solving the coupling interference between the independently modulated spectrum corresponding to different eigenvalues.