Synthetic Data Generation using Diffusion Models for ML-based Lightpath Quality of Transmission Estimation Under Extreme Data Scarcity

Generative diffusion models are gaining attention as a promising solution for synthetic data generation, offering a distinct advantage over traditional statistical methods and basic generative models. This work focuses on evaluating the effectiveness of such models in the context of estimating Lightpath Quality of Transmission (QoT) in optical networks, especially when real data availability is strongly limited. Numerical results demonstrate that leveraging diffusion models for data augmentation can significantly improve QoT classification accuracy and F1-score when available data are limited to a few dozens of samples. These findings highlight the potential of generative diffusion models in improving data-driven tasks for optical network management under sparse data conditions.