A Modified Regular Perturbation Model for the Single-Span Fiber Transmission Using Learnable Methods

Abstract

In fiber optic communication systems, the dispersion and nonlinear interaction of optical signals are critical to modeling fiber optic communication, and the regular perturbation (RP) model is a simplified modeling method composed of parallel branches, which has obvious advantages in deep learning backpropagation. In this paper, we propose a simplified single-mode fiber signal transmission model based on the RP model, which significantly improves the fitting accuracy of the model for dispersion and nonlinear interactions at the same complexity by adding trainable parameters to the standard RP model. We explain in the paper that this improvement is applicable to dual-polarization systems and still effective under the conditions of large launch power, without dispersion management, and containing amplified spontaneous emission (ASE) noise. The model uses the standard split-step Fourier method (SSFM) to generate labels and updates parameters through gradient descent method. When transmitting a dual-polarization signal with a launch power of 13 dBm, the modified regular perturbation (MRP) model proposed in the paper can reduce the fitting errors by more than 75% compared to the standard RP model after transmitting through a 120 km standard single-mode fiber.