Machine learning for optical fiber communication systems: An introduction and overview

Abstract

Optical networks generate a vast amount of diagnostic, control, and performance monitoring data. When information is extracted from these data, reconfigurable network elements and reconfigurable transceivers allow the network to adapt not only to changes in the physical infrastructure but also to changing traffic conditions. Machine learning is emerging as a disruptive technology for extracting useful information from these raw data to enable enhanced planning, monitoring, and dynamic control. We provide a survey of the recent literature and highlight numerous promising avenues for machine learning applied to optical networks, including explainable machine learning, digital twins, and approaches in which we embed our knowledge into machine learning such as physics-informed machine learning for the physical layer and graph-based machine learning for the networking layer.