Predicting failures in fiber optic information transmission systems with support of machine learning

The use of machine learning methods in fiber-optic information transmission systems (FOITS) is considered. The article discusses the basic operating principles of fiber optic systems and the problems they face, such as noise, nonlinear effects, and degradation of transmitted information. Describes various machine learning techniques used in FOITS to control and monitor performance, prevent intelligent decisions, and suppress nonlinear fiber optic noise. Approaches used in machine learning are presented, such as neural networks, classification and regression algorithms, their application in the analysis and optimization of FOITS, such as neural networks, support vector machines, classification and regression algorithms, their application in the analysis and optimization of fiber optic systems. This paper proposes a method for monitoring performance and predicting failures in optical networks based on machine learning. The results obtained allow us to draw conclusions about the most effective methods for predicting failures, which is of great practical importance for ensuring the reliability of communication networks and minimizing downtime.