Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Accurate Quality of Transmission Estimation With Machine Learning

Not Accessible

Your library or personal account may give you access

Abstract

In optical transport networks the quality of transmission (QoT) is estimated before provisioning new connections or upgrading existing ones. Traditionally, a physical layer model (PLM) is used for QoT estimation coupled with high margins to account for the model inaccuracy and the uncertainty in the evolving physical layer conditions. Reducing the margins increases network efficiency but requires accurate QoT estimation. We present two machine learning (ML) approaches to formulate such an accurate QoT estimator. We gather physical layer feedback, by monitoring the QoT of existing connections, to understand the actual physical conditions of the network. These data are used to train either the input parameters of a PLM or a machine learning model (ML-M). The proposed ML methods account for variations and uncertainties in equipment parameters, such as fiber attenuation, dispersion, and nonlinear coefficients, or amplifier noise figure per span, which are typical in deployed networks. We evaluated the accuracy of the proposed methods under various uncertainty scenarios and compared them to QoT estimators proposed in the literature. The results indicate that our estimators yield excellent accuracy with a relatively small amount of data, outperforming other prior estimators.

© 2019 Optical Society of America

Full Article  |  PDF Article
More Like This
Performance comparisons between machine learning and analytical models for quality of transmission estimation in wavelength-division-multiplexed systems [Invited]

Jianing Lu, Gai Zhou, Qirui Fan, Dengke Zeng, Changjian Guo, Linyue Lu, Jianqiang Li, Chongjin Xie, Chao Lu, Faisal Nadeem Khan, and Alan Pak Tao Lau
J. Opt. Commun. Netw. 13(4) B35-B44 (2021)

Machine Learning Models for Estimating Quality of Transmission in DWDM Networks

Rui Manuel Morais and João Pedro
J. Opt. Commun. Netw. 10(10) D84-D99 (2018)

Machine learning for quality of transmission: a picture of the benefits fairness when planning WDM networks

Matteo Lonardi, Jelena Pesic, Thierry Zami, Emmanuel Seve, and Nicola Rossi
J. Opt. Commun. Netw. 13(12) 331-346 (2021)

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Figures (9)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Equations (6)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All Rights Reserved