Azzimonti, Dario Filippo and Rottondi, Cristina and Giusti, Alessandro and Tornatore, Massimo and Bianco, Andrea (2021) Comparison of domain adaptation and active learning techniques for quality of transmission estimation with small-sized training datasets [Invited]. Journal of Optical Communications and Networking, 13 (1). A56. ISSN 1943-0620
Full text not available from this repository.Abstract
Machine learning (ML) is currently being investigated as an emerging technique to automate quality of transmission (QoT) estimation during lightpath deployment procedures in optical networks. Even though the potential network-resource savings enabled by ML-based QoT estimation has been confirmed in several studies, some practical limitations hinder its adoption in operational network deployments. Among these, the lack of a comprehensive training dataset is recognized as a main limiting factor, especially in the early network deployment phase. In this study, we compare the performance of two ML methodologies explicitly designed to augment small-sized training datasets, namely, active learning (AL) and domain adaptation (DA), for the estimation of the signal-to-noise ratio (SNR) of an unestablished lightpath. This comparison also allows us to provide some guidelines for the adoption of these two techniques at different life stages of a newly deployed optical network infrastructure. Results show that both AL and DA permit us, starting from limited datasets, to reach a QoT estimation capability similar to that achieved by standard supervised learning approaches working on much larger datasets. More specifically, we observe that a few dozen additional samples acquired from selected probe lightpaths already provide significant performance improvement for AL, whereas a few hundred samples gathered from an external network topology are needed in the case of DA.
| Item Type: | Scientific journal article, Newspaper article or Magazine article |
|---|---|
| Subjects: | Computer sciences > Artificial intelligence > Machine learning |
| Department/unit: | Dipartimento tecnologie innovative > Istituto Dalle Molle di studi sull’intelligenza artificiale USI-SUPSI |
| Depositing User: | Dario Filippo Azzimonti |
| Date Deposited: | 03 Oct 2023 10:44 |
| Last Modified: | 03 Oct 2023 10:47 |
| URI: | http://repository.supsi.ch/id/eprint/14629 |
Actions (login required)
 |
View Item |