Machine Learning Algorithms for Prediction of the Quality of Transmission in Optical Networks

Stanisław Kozdrowski; Paweł Cichosz; Piotr Paziewski; Sławomir Sujecki

doi:10.3390/e23010007

Machine Learning Algorithms for Prediction of the Quality of Transmission in Optical Networks

Entropy (Basel). 2020 Dec 22;23(1):7. doi: 10.3390/e23010007.

Authors

Stanisław Kozdrowski¹, Paweł Cichosz¹, Piotr Paziewski¹, Sławomir Sujecki²

Affiliations

¹ Computer Science Institute, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland.
² Telecommunications and Teleinformatics Department, Wroclaw University of Science and Technology, Wyb. Wyspianskiego 27, 50-370 Wroclaw, Poland.

Abstract

Increasing demand in the backbone Dense Wavelength Division (DWDM) Multiplexing network traffic prompts an introduction of new solutions that allow increasing the transmission speed without significant increase of the service cost. In order to achieve this objective simpler and faster, DWDM network reconfiguration procedures are needed. A key problem that is intrinsically related to network reconfiguration is that of the quality of transmission assessment. Thus, in this contribution a Machine Learning (ML) based method for an assessment of the quality of transmission is proposed. The proposed ML methods use a database, which was created only on the basis of information that is available to a DWDM network operator via the DWDM network control plane. Several types of ML classifiers are proposed and their performance is tested and compared for two real DWDM network topologies. The results obtained are promising and motivate further research.

Keywords: artificial intelligence; machine learning; machine learning classifiers; optical networks; quality of transmission.