Application of machine learning methods  in provisioning  of DWDM channels

Piotr Paziewski; Sławomir Sujecki; Stanisław Kozdrowski

doi:10.1117/12.2536656

13 September 2019 Application of machine learning methods in provisioning of DWDM channels

Piotr Paziewski, Sławomir Sujecki, Stanisław Kozdrowski

Proceedings Volume 11204, 14th Conference on Integrated Optics: Sensors, Sensing Structures, and Methods; 1120407 (2019) https://doi.org/10.1117/12.2536656
Event: Fourteenth Integrated Optics-Sensors, Sensing Structures and Methods Conference, 2019, Szcyrk-Gliwice, Poland

Abstract

Complexity and size of modern optic-fiber networks start to challenge the traditional methods of managing them and yet majority of telecommunication companies still report rapid growth of their optical networks. One of essential problems in managing optic-fiber networks is calculating the Quality of Transmission (QoT) of given path in network. The unit responsible for this task is Optical Performance Unit (OPU) which communicates with Network Management System (NMS). OPU's task is to determine whether it is possible to transmit signal through a given path. Modern OPUs are still operating based on traditional algorithms e.g. these systems take into consideration known physics rules and information about the network parameters, calculating transmission losses for each path. Main parameter that determines the OPUs result is Optical Signal to Noise Ratio (OSNR). However, measuring its value from NMS level is often not practical. An alternative solution to this problem might prove the application of Machine Learning (ML) algorithms for the estimation of OSNR. In this contribution an application of Artificial Neural Network (ANN) to an evaluation of OSNR in an optical Dense Wavelength Division Multiplexing (DWDM) network is investigated.

Citation Download Citation

Piotr Paziewski, Sławomir Sujecki, and Stanisław Kozdrowski "Application of machine learning methods in provisioning of DWDM channels", Proc. SPIE 11204, 14th Conference on Integrated Optics: Sensors, Sensing Structures, and Methods, 1120407 (13 September 2019); https://doi.org/10.1117/12.2536656

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available