Machine learning assisted EDFA modelling for gain and ASE prediction

Abstract

Machine Learning methods and algorithms have found use in a vast number of industries, including the optical networks industries. One of the key components of any optical network is the Erbium Doped Fibre Amplifier, or EDFA. EDFAs have variable wavelength dependent output gain profiles, and are a major source of noise and nonlinear impairments. Thus, EDFA modelling becomes necessary for optical network provisioning and updation, and machine learning methods are the perfect tool for the same. By means of this study, first, a comprehensive and highly accurate EDFA model is created by solving the coupled signal propagation equations of the EDFA. Since the model is complex, and there are vast numbers of channel configurations, varying numbers of spans as well as EDFA defects, an ML model is later trained to predict the output gain as well as ASE noise profiles of the EDFA, given the input channel configurations and the associated pump powers.