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

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.