Abstract
An optical transmission link is generally composed of optical fibers, optical amplifiers, and optical filters.
These optical components are conventionally monitored by analog measurement instruments such
as an optical time domain reflectometry and an optical spectrum analyzer. We review recent studies that
attempt to extract these components’ characteristics only from digital signal processing of data-carrying
signals in a system identification manner. The key is to leverage the similarity between structures of the
split-step Fourier method for the nonlinear Schrödinger equation and neural networks. By combining
these existing model and data-driven approach, we have demonstrated a monitoring method that extracts
longitudinal distributions of optical components’ characteristics such as optical fiber loss/dispersion profiles,
gain spectra of multiple optical amplifiers, and responses of multiple optical filters without any analog
measurement devices. This approach will thus facilitate automated establishment and management
of optical networks.
