Deep Learning Waveform Channel Modeling for Wideband Optical Fiber Transmission: Model Comparisons, Challenges and Potential Solutions

Abstract

Fast and accurate waveform simulation is critical for understanding fiber channel characteristics, developing digital signal processing (DSP) technologies, optimizing optical network configurations, and advancing the optical fiber transmission system towards wideband. Deep learning (DL) has emerged as a powerful tool for waveform modeling, offering high accuracy and low complexity compared to traditional split-step Fourier method (SSFM), due to its strong nonlinear fitting capabilities and efficient parallel computation. However, most DL methods are designed for few-channel and low-rate WDM systems, leaving their scalability to wideband systems uncertain. Moreover, the lack of a standardized accuracy evaluation method and the inconsistent results between waveform errors and transmission performance errors, hinders fair comparisons of various DL schemes. In this paper, we introduce a DSP-assisted accuracy evaluation method integrated with nonlinear DSP, providing a fair benchmark for evaluating the accuracy of DL models. Using this method, we conduct a comprehensive comparison of DL schemes, ranging from simple configurations to more complex wideband setups. The feature decoupled distributed method combining with bidirectional long short-term memory achieves the better performance compared to other DL schemes. Furthermore, in scenarios with more-channel and higher-rate, the performance advantages of FDD-BiLSTM will be further improved. However, as the number of channels and symbol rates increase, the performance of FDD-BiLSTM still gradually deteriorate. We analyze these challenges from three perspectives: the more intricate linear and nonlinear effects, the higher sampling rate required for SSFM. To address these challenges, we discuss potential solutions from two aspects: incorporating more prior physical knowledge and optimizing the structure of DL models.