Efficient waveform modeling of 10,000-km ultra-long submarine optical fiber channels based on cascaded training...

Submarine optical fiber systems underpin global information infrastructure and continuous development is required, making accurate channel modeling essential for the evolution of submarine optical fiber systems and digital signal processing (DSP) evaluation. Traditional numerical approaches, such as the split-step Fourier method (SSFM) is hugely time-consuming. Deep learning (DL)-based models offer both rapid simulation and prediction capabilities as well as the advantage of providing waveform information; however, in long-haul fiber-optic link simulation scenarios spanning thousands of kilometers, the accuracy of cascaded DL models degrades sharply. To overcome these limitations, we propose a Cascaded Training Neural Network (CTNN) method designed to significantly improve the modeling accuracy of DL-based optical fiber models in ultra-long-haul scenarios. In this architecture, the models are cascaded during the training phase, allowing the long-distance loss to be used for updating the network parameters. Compared with the high-accuracy SSFM, the normalized mean square error (NMSE) of the waveform simulated by the CTNN-based model remains below 0.007 even after 10,000 km of propagation in long-haul submarine fiber wavelength-division multiplexing (WDM) systems, whereas the NMSE of models without CTNN is 0.08. Moreover, the model's prediction errors for both the signal-to-noise ratio (SNR) and Q-factor remain below only 0.15 dB after applying nonlinear or linear compensation algorithms at the receiver. Meanwhile, the simulation time was reduced to 1% of that of the SSFM method. The CTNN model enables fast and accurate modeling of submarine optical fiber WDM systems with transmission distances ranging from several thousand to over ten thousand kilometers based on DL methods while providing waveform information.