Abstract

Estimating the quality of transmission (QoT) of a lightpath before its establishment is a critical procedure for efficient design and management of optical networks. Recently, supervised machine learning (ML) techniques for QoT estimation have been proposed as an effective alternative to well-established, yet approximated, analytic models that often require the introduction of conservative margins to compensate for model inaccuracies and uncertainties. Unfortunately, to ensure high estimation accuracy, the training set (i.e., the set of historical field data, or “samples,” required to train these supervised ML algorithms) must be very large, while in real network deployments, the number of monitored/monitorable lightpaths is limited by several practical considerations. This is especially true for lightpaths with an above-threshold bit error rate (BER) (i.e., malfunctioning or wrongly dimensioned lightpaths), which are infrequently observed during network operation. Samples with above-threshold BERs can be acquired by deploying probe lightpaths, but at the cost of increased operational expenditures and wastage of spectral resources. In this paper, we propose to use active learning to reduce the number of probes needed for ML-based QoT estimation. We build an estimation model based on Gaussian processes, which allows iterative identification of those QoT instances that minimize estimation uncertainty. Numerical results using synthetically generated datasets show that, by using the proposed active learning approach, we can achieve the same performance of standard offline supervised ML methods, but with a remarkable reduction (at least 5% and up to 75%) in the number of training samples.

© 2019 Optical Society of America

Full Article  |  PDF Article
OSA Recommended Articles
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)

Experimental Demonstration of Machine-Learning-Aided QoT Estimation in Multi-Domain Elastic Optical Networks with Alien Wavelengths

Roberto Proietti, Xiaoliang Chen, Kaiqi Zhang, Gengchen Liu, M. Shamsabardeh, Alberto Castro, Luis Velasco, Zuqing Zhu, and S. J. Ben Yoo
J. Opt. Commun. Netw. 11(1) A1-A10 (2019)

Accurate Quality of Transmission Estimation With Machine Learning

Ippokratis Sartzetakis, Konstantinos (Kostas) Christodoulopoulos, and Emmanouel (Manos) Varvarigos
J. Opt. Commun. Netw. 11(3) 140-150 (2019)

References

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

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

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 OSA member, or as an authorized user of your institution.

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

Figures (7)

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

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

Tables (9)

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

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

Equations (7)

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

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