Abstract

We conduct a comprehensive comparative study of quality-of-transmission (QoT) estimation for wavelength-division-multiplexed systems using artificial neural network (ANN)-based machine learning (ML) models and Gaussian noise (GN) model-based analytical models. To obtain the best performance for comparison, we optimize all the system parameters for GN-based models in a brute-force manner. For ML models, we optimize the number of neurons, activation function, and number of layers. In simulation settings with perfect knowledge of system parameters and communication channels, GN-based analytical models generally outperform ANN models even though GN models are less accurate on the side channels due to the local white-noise assumption. In experimental settings, however, inaccurate knowledge of various link parameters degrades GN-based models, and ML generally estimates the QoT with better accuracy. However, ML models are temporally less stable and less generalizable to different link configurations. We also briefly study potential network capacity gains resulting from improved QoT estimators and reduced operating margins.

© 2021 Optical Society of America

Full Article  |  PDF Article
More Like This
Associating machine-learning and analytical models for quality of transmission estimation: combining the best of both worlds

Emmanuel Seve, Jelena Pesic, and Yvan Pointurier
J. Opt. Commun. Netw. 13(6) C21-C30 (2021)

Machine-learning-based EDFA gain estimation [Invited]

Jiakai Yu, Shengxiang Zhu, Craig L. Gutterman, Gil Zussman, and Daniel C. Kilper
J. Opt. Commun. Netw. 13(4) B83-B91 (2021)

Machine learning techniques for quality of transmission estimation in optical networks

Yvan Pointurier
J. Opt. Commun. Netw. 13(4) B60-B71 (2021)

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 (10)

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

Equations (2)

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