Application of machine learning methods  in provisioning  of DWDM channels

Paziewski, Piotr; Sujecki, S.; Kozdrowski, Stanisław

doi:10.1117/12.2536656

Cited by 1 publication

(2 citation statements)

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“…Therefore, in this article we propose applying machine learning methods to estimate the QoT of optical channels. It is noted that ML has been already used to address the problem of optical link QoT assessment by other authors [ 8 , 15 , 27 , 28 , 29 ], also for non-coherent networks [ 30 ]. However, our approach is based on real data that is easily available to a DWDM network operator through the control plane and does not use data that is only available to DWDM equipment providers and fiber providers.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the main advantage of the presented approach is that it can be easily implemented by a network operator. As will be explained in the next paragraph, such an approach imposes some constraints on the selection of suitable ML methods, which makes the ML problem considered in this contribution distinctly different from the one addressed in [ 8 , 15 , 27 , 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

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

Kozdrowski

Cichosz

Paziewski

et al. 2020

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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.

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