Physics-Informed Machine Learning for EDFA: Parameter Identification and Gain Estimation

TL;DR

This paper introduces a physics-informed machine learning approach that accurately identifies EDFA parameters and estimates gain with less data, improving optical communication system modeling and optimization.

Contribution

It proposes a novel PINN-based method for EDFA parameter identification and gain estimation, reducing data needs and enhancing accuracy over existing models.

Findings

Effective parameter identification from limited data

Accurate gain estimation with mean absolute error of 0.127 dB

Validated approach in practical experimental setup

Abstract

As the key component that facilitates long-haul transmission in optical fiber communications by increasing capacity and reducing costs, accurate characterization and gain settings of erbium-doped fiber amplifiers (EDFAs) are essential for quality of transmission estimation and system configuration optimization. However, it is difficult to construct accurate and reliable EDFA models due to complex physical mechanisms and dynamic loading conditions. Although some mathematical and data-driven models have been proposed, their practical applications will face limitations of intricate parameter measurements and high data requirements, respectively. To overcome limitations of both methods, a physics-informed machine learning (PIML) method for parameter identification and gain estimation of EDFA is proposed, which greatly reduces the data requirements by embedding physical prior knowledge in the neural network. In this approach, the gain of EDFA can be accurately estimated by a physics-informed neural network (PINN)-based forward model when parameters including absorption, gain, saturation, and background loss are known. For practical scenarios where parameters are unknown, PINN-based inverse models are established first to identify actual values of parameters from only several sets of input-output data pairs, and PINN-based forward models are accordingly established for gain estimation with identified values. Moreover, an experimental system is constructed to verify the feasibility and performance of proposed method in practical scenarios. Results show that PIML-based method can effectively identify physical parameters from measured data, and better gain estimation results are achieved with mean absolute error of 0.127 dB and standard deviation of 0.065 dB using identified values than typical values of parameters.