BERT-Based Modeling Method for Long-Distance PDM Transmission Channel

Abstract

Fast and accurate modeling techniques are crucial for improving the performance of long-distance polarization division multiplexing (PDM) transmission. Traditional methods, such as the Split-Step Fourier Method (SSFM), suffer from high computational complexity, especially for long-distance and large-scale data. To overcome this challenge, a BERT-based modeling method for long-distance PDM transmission channel is proposed. The BERT-based method enables fast and accurate modeling of transmission channels without requiring iterative processes, in which BERT is a deep learning model leveraging the attention mechanism. Numerical experiment results show that the fitting waveforms of the proposed method closely match the actual waveforms over 1200 km across various launch powers, modulation formats and erbium-doped fiber amplifier (EDFA) noise, achieving normalized mean squared errors below 0.003. Furthermore, the computational complexity of the BERT-based method is approximately 1/3800 of that of the SSFM.