Power evolution prediction of bidirectional Raman amplified WDM system based on PINN

Abstract

We propose using physical-informed neural network (PINN) for power evolution prediction in bidirectional Raman amplified WDM systems with Rayleigh backscattering (RBS). Unlike models based on data-driven machine learning, PINN can be effectively trained without preparing a large amount of data in advance and can learn the potential rules of power evolution. Compared to previous applications of PINN in power prediction, our model considers bidirectional Raman pumping and RBS, which is more practical. We experimentally demonstrate power evolution prediction of 200 km bidirectional Raman amplified wavelength-division multiplexed (WDM) system with 47 channels and 8 pumps using PINN. The maximum prediction error of PINN compared to experimental results is only 0.38 dB, demonstrating great potential for application in power evolution prediction. The power evolution predicted by PINN shows good agreement with the results simulated by traditional numerical method, but its efficiency is more suitable for establishing models and calculating noise, providing convenience for subsequent power configuration optimization.