Comparison of domain adaptation and active learning techniques for quality of transmission estimation with small-sized training datasets [Invited]

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.