Fourier Neural Operator Accelerated Fast and Accurate Optical Fiber Transmission Modeling

作者：T. K. Zhang, Yutong Pan, Jiayu Zheng, Xiang Cai, Danshi Wang, Xian Zhou, Fan Zhang · 发表于：Journal of Lightwave Technology · 年份：2025 · DOI：10.1109/jlt.2025.3529043 · 被引用次数：8 · 研究领域：Optical Network Technologies、Advanced Photonic Communication Systems、Advanced Optical Network Technologies

Optical signal transmission in optical fiber links is governed by nonlinear Schrödinger equation (NLSE). Fast and accurate modeling the propagation of optical signals is crucial for designing digital signal processing (DSP) algorithms, and optimizing the system performance. In this article, we combine Fourier neural operator (FNO) with the traditional numerical split-step Fourier method (SSFM) for solving NLSE, and propose a novel modeling scheme called simplified SSFM (S-SSFM) + FNO, which can perform fast modelling of long-distance transmission of high baud rate signals. The proposed method has been validated for accuracy, generalization, and rapid computational speed in both single-channel and wavelength-division-multiplexing (WDM) systems. In single-channel scenarios with transmission distances up to 2400 km, accounting for varying signal powers, the signal-to-noise ratio (SNR) errors of the proposed scheme remain consistently below 0.1dB. Remarkably, the calculation time for this method amounts to only 7.5% of the constant step size SSFM (C-SSFM) approach with a step size of 0.1 km, and 17.5% of the nonlinear phase SSFM (NP-SSFM) with a maximum phase shift value at 0.05 degree. Furthermore, the neural network (NN) utilized in this scheme demonstrates a notable advantage in training speed, achieving convergence in a timeframe comparable to the modeling phase. For WDM systems, the superior computational efficiency of S-SSFM+FNO becomes increasingly evident. With 21 channel...