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 datadriven 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, selfphase 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. Index Terms-data-driven, deep learning, deep neural network, fiber channel modeling, fiber optics systems, and split-step Fourier method I. INTRODUCTION IMULATIONS are vital in optical communication system designs[1, 2]. Conventional optical communication system simulations are based on a series of blocks that are characterized by rigorous numerical models, including a laser, modulator, fiber channel, optical amplifier, filter, detector, and analyzer.[3] Therefore, for the model-driven method, it is a systematic engineering task that requires expert knowledge to construct a comprehensive and complete optical communication system simulation. This is why business optical communication simulation software are usually non-open and expensive. Furthermore, the computation complexity of conventional simulations can be very high due to the nestedfunction structure and the repeated iterative operations, especially the split-step Fourier method (SSFM) which is performed to model the fiber channel by solving the nonlinear Schrödinger equation (NLSE) [1,4,5]. Therefore, a method for optical fiber channel modeling with relatively low computation complexity is quite valuable.Deep learning (DL) is a powerful tool that has dramatically This paragraph of the first footnote will contain the date on which you submitted your paper for review, which is populated by IEEE.

Small Sample Fiber Full State Diagnosis Based on Fuzzy Clustering and Improved ResNet Network

“…In recent years, deep learning has made significant advancements in the field of fault detection, with deep convolutional neural networks (CNNs) demonstrating powerful capabilities in feature extraction and classification. The literature [13] investigated the application of data-driven fiber modeling methods based on deep neural networks in optical communication systems, providing flexibility and generality to fiber modeling while reducing computational complexity. The literature [14] utilized fiber sensing technology and deep learning algorithms for fault detection, demonstrating excellent performance in automatic feature extraction of faults.…”

“…With reduced parameters of the neural network, the resulting feature vectors are then fed into the classification layer for classification. In this study, we adopted the ResNet [13], which has excellent feature extraction capabilities. Compared to other network architectures, the ResNet's residual structure (shortcut connection) allows direct transmission of input data information to the output, addressing the problems of gradient vanishing and accuracy degradation that arise with increasing network depth.…”

End-to-End Deep Learning of Joint Geometric Probabilistic Shaping Using a Channel-Sensitive Autoencoder

“…At the transmitter, neural networks (NNs) can be used for constellation shaping (CS), which includes geometric shaping (GS) [6] and probabilistic shaping (PS) [7], to promote transmission flexibility. In terms of transmission channels, NNs exhibit strong channel fitting abilities, providing rapid channel simulation [8][9][10][11]. At the receiver, NNs are widely applied for nonlinear equalization and modulation format identification to compensate for signal distortion [12][13][14].…”