Active learning for OPM in FMF systems

Abstract

Optical performance monitoring (OPM) is essential to guarantee the robust and reliable operation of few-mode fiber (FMF)-based transmission. The available OPM methods including the analytical models such as the enhanced Gaussian noise model provide high accuracy along with high computational complexity which makes them improper for real-time implementations. As an alternative approach, machine learning (ML)-based OPM removes this barrier at the cost of leveraging a large training dataset. However, generating a field or synthetic dataset for FMF-based transmission is very hard and time-consuming. As a specific ML deployment, active learning (AL) is designed to work with a small training dataset, therefore, in this paper, we employ AL for OPM in FMF-based transmission. Results indicate that the proposed AL-based OPM can properly estimate the generalized signal-to-noise ratio by using a very small training dataset and achieve the root mean squared error similar to that obtained by working on large training datasets.