A Comprehensive Study of Machine Learning Application to Transmission Quality Assessment in Optical Networks.

This paper examines applying machine learning to the assessment of the quality of the transmission in optical networks. The motivation for research into this problem derives from the fact that the accurate assessment of transmission quality is key to an effective management of an optical network by a network operator. In order to facilitate a potential implementation of the proposed solution by a network operator, the training data for the machine learning algorithms are directly extracted from an operating network via a control plane. Particularly, this work focuses on the application of single class and binary classification machine learning algorithms to optical network transmission quality assessment. The results obtained show that the best performance can be achieved using gradient boosting and random forest algorithms. [ABSTRACT FROM AUTHOR]