@inproceedings{a8a659e7f1b240a4b12a6b237fa46029,
title = "An overview on machine learning-based solutions to improve lightpath QoT estimation",
abstract = "Estimating lightpath Quality of Transmission (QoT) is crucial in network design and service provisioning. Recent studies have turned to Machine Learning (ML) techniques to improve the accuracy of QoT estimation. We distinguish two categories of solutions: the first category aims to build ML-based QoT estimation models that outperform the analytical model while the second category uses ML algorithms to reduce uncertainties on parameters provided as input to analytical model. In this overview, we describe the solutions in each category and discuss their practical feasibility and added benefit for operational networks.",
keywords = "Machine learning, QoT, WDM networks",
author = "R. Ayassi and A. Triki and M. Laye and N. Crespi and R. Minerva and C. Catanese",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 22nd International Conference on Transparent Optical Networks, ICTON 2020 ; Conference date: 19-07-2020 Through 23-07-2020",
year = "2020",
month = jul,
day = "1",
doi = "10.1109/ICTON51198.2020.9203755",
language = "English",
series = "International Conference on Transparent Optical Networks",
publisher = "IEEE Computer Society",
booktitle = "2020 22nd International Conference on Transparent Optical Networks, ICTON 2020",
}