Abstract

Machine learning (ML)-assisted solutions for quality of transmission (QoT) estimation or classification have received significant attention in recent years. However, due to the unavailability of large and well-structured datasets, individual research groups need to create and use their own datasets for validating their proposed solutions. Therefore, the reported results (obtained using different datasets) are difficult to reproduce and hardly comparable. Regardless of this limitation, the unavailability of a technique to be followed by different research groups for the explainability of the dataset makes it even harder to validate the developed ML-assisted solutions across different papers. In this work, we present a publicly available dataset collection to open the problem of data-driven QoT estimation to the ML community. The dataset collection allows various solutions presented by different research groups to be compared. Furthermore, we present techniques to visualize and evaluate datasets for QoT estimation. The presented visualizations can also deliver deep insight into the error analysis of ML models. We apply these new methods to evaluate an artificial neural network on different datasets. The results show the relevance of the presented visualizations for comparing different approaches and different datasets. The proposed methods enable the comparison and validation of different ML-based solutions and published datasets.

© 2021 Optical Society of America

Full Article  |  PDF Article
More Like This
Automated training dataset collection system design for machine learning application in optical networks: an example of quality of transmission estimation

Jianing Lu, Qirui Fan, Gai Zhou, Linyue Lu, Changyuan Yu, Alan Pak Tao Lau, and Chao Lu
J. Opt. Commun. Netw. 13(11) 289-300 (2021)

Lightpath QoT computation in optical networks assisted by transfer learning

Ihtesham Khan, Muhammad Bilal, M. Umar Masood, Andrea D’Amico, and Vittorio Curri
J. Opt. Commun. Netw. 13(4) B72-B82 (2021)

Machine learning regression for QoT estimation of unestablished lightpaths

Memedhe Ibrahimi, Hatef Abdollahi, Cristina Rottondi, Alessandro Giusti, Alessio Ferrari, Vittorio Curri, and Massimo Tornatore
J. Opt. Commun. Netw. 13(4) B92-B101 (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 Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica 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 Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica 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 Optica member, or as an authorized user of your institution.

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

Tables (8)

You do not have subscription access to this journal. Article tables 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 (7)

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