Fourier Neural Operator for Accurate Optical Fiber Modeling With Low Complexity

In this paper, a novel deep learning architecture, Fourier neural operator (FNO), has been introduced to ap-proximate the nonlinear Schrodinger equation which characterizes fiber transmission impairments such as fiber attenuation, chromatic dispersion, nonlinear impairments, etc. The proposed scheme is firstly verified via numerical simulation in 28-GBaud 4QAM/16QAM/64QAM systems over 1200-km standard single mode fiber (SSMF) under different launch powers. The simulation results show that the mean square errors (MSEs) are less than 0.002 and the effective SNRs differences between the proposed FNO and SSFM are all within 1 dB at 1200 km SSMF with the launching power from -5 dBm to 5 dBm. The performance of the proposed FNO model is further evaluated by a conceptual experiment. The experimental results show that the Q-factor performance of the FNO-based channel model is close to that of 1200 km experimental SSMF transmission link with the launching power from -3 dBm to 5 dBm.

Automated summary

This paper introduces a novel deep learning architecture, Fourier neural operator (FNO), to approximate the nonlinear Schrodinger equation for characterizing various fiber transmission impairments. The proposed scheme is validated through numerical simulation in 28-GBaud systems over 1200-km standard single mode fiber (SSMF) with different launch powers. The simulation results demonstrate that the proposed FNO achieves low mean square errors (MSEs) and maintains effective signal-to-noise ratios (SNRs) comparable to SSFM within 1 dB difference.