Abstract

Artificial intelligence (AI) and machine learning (ML) continue to demonstrate substantial capabilities in solving a wide range of optical-network-related tasks such as fault management, resource allocation, and lightpath quality of transmission (QoT) estimation. However, the focus of the research community has been centered on ML models’ predictive capabilities, neglecting aspects related to models’ understanding, i.e., to interpret how the model reasons and makes its predictions. This lack of transparency hinders the understanding of a model’s behavior and prevents operators from judging, and hence trusting, the model’s decisions. To mitigate the lack of transparency and trust in ML, explainable AI (XAI) frameworks can be leveraged to explain how a model correlates input features to its outputs. In this paper, we focus on the application of XAI to lightpath QoT estimation. In particular, we exploit Shapley additive explanations (SHAP) as the XAI framework. Before presenting our analysis, we provide a brief overview of XAI and SHAP, then discuss the benefits of the application of XAI in networking and survey studies that apply XAI to networking tasks. Then, we model the lightpath QoT estimation problem as a supervised binary classification task to predict whether the value of the bit error rate associated with a lightpath is below or above a reference acceptability threshold and train an ML extreme gradient boosting model as the classifier. Finally, we demonstrate how to apply SHAP to extract insights about the model and to inspect misclassifications.

© 2022 Optica Publishing Group

QoT estimation using EGN-assisted machine learning for multi-period network planning

Jasper Müller, Sai Kireet Patri, Tobias Fehenberger, Helmut Griesser, Jörg-Peter Elbers, and Carmen Mas-Machuca
J. Opt. Commun. Netw. 14(12) 1010-1019 (2022)

Lightpath Establishment Assisted by Offline QoT Estimation in Transparent Optical Networks

Nicola Sambo, Yvan Pointurier, Filippo Cugini, Luca Valcarenghi, Piero Castoldi, and Ioannis Tomkos
J. Opt. Commun. Netw. 2(11) 928-937 (2010)

ML-assisted QoT estimation: a dataset collection and data visualization for dataset quality evaluation

Geronimo Bergk, Behnam Shariati, Pooyan Safari, and Johannes K. Fischer
J. Opt. Commun. Netw. 14(3) 43-55 (2022)

Evolutionary neuron-level transfer learning for QoT estimation in optical networks

Yuhang Zhou, Zhiqun Gu, Jiawei Zhang, and Yuefeng Ji
J. Opt. Commun. Netw. 16(4) 432-448 (2024)

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)