Data-Driven Method for Nonlinear Optical Fiber Channel Modeling Based on Deep Neural Network

Abstract

Recently, data-driven fiber channel modeling methods based on deep learning have been proposed in optical communication system simulations. We investigate a new data-driven method based on the deep neural network (DNN) to model the nonlinear fiber channel with the characteristics of attenuation, chromatic dispersion, amplified spontaneous emission noise, self-phase modulation (SPM), and cross-phase modulation (XPM). Demonstration in multiple dimensions, including constellations, optical waveforms, spectra, and the normalized mean square error, shows that DNN can approach the transfer function of the fiber channel accurately. Additionally, the DNN shows good generalization for modulation formats and wavelength schemes. Besides, the time complexity of DNN-based method for modeling nonlinear fiber channel is reduced significantly (96.5%) compared to the conventional model-driven method, which is based on the split-step Fourier method. This work demonstrates that the DNN can model accurately the nonlinear fiber channel that takes account of both SPM and XPM. Therefore, it can contribute to the application of data-driven methods in modern optical communication system simulations and designs.