Datasets for QoT estimation in SDM networks

The advancement of machine learning (ML)-assisted solutions for monitoring and performance analysis in space-division multiplexing (SDM) networks has been significantly constrained by a shortage of large, well-structured, and publicly available datasets. As a result, researchers often rely on custom-built datasets, making reproducibility difficult and complicating cross-comparisons of proposed methods. To address this gap, we introduce 18 novel, to our knowledge, and publicly available quality of transmission (QoT) datasets, designed specifically for SDM networks. These datasets cover a wide range of SDM configurations, incorporating multiple fiber types, switching strategies, and two distinct network topologies. By offering consistent benchmarks, these datasets aim to support the development of ML-driven automation in SDM networks, making studies more efficient, reliable, and comparable. To demonstrate practical applications, we developed and evaluated two ML models—a classification model and a regression model—using these datasets to predict QoT outcomes. The findings illustrate the importance of the proposed datasets in benchmarking various ML-based approaches, facilitating comparison and validation across studies, and advancing ML-based network automation. This work accelerates progress toward more operationally efficient and high-performing SDM networks.