A Comprehensive Study of Machine Learning Application to Transmission Quality Assessment in Optical Networks
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Optical Network Technologies · Advanced Photonic Communication Systems · Advanced Optical Network Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland
Telecommunications and Teleinformatics Department, Wroclaw University of Science and Technology, Wyb. Wyspianskiego 27, 50-370 Wroclaw, Poland
Faculty of Electronics, Military University of Technology, 00-908 Warsaw, Poland
References (45)
Show source-provided references
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