Uncertainty Decomposition for Machine-Learning-Based QoT Estimation in Coherent Optical Networks
Machine learning (ML) has shown strong performance for Quality of Transmission (QoT) estimation in optical networks, but reliability under out-of-distribution (OOD) conditions remains a key operational concern. We propose an uncertainty-aware QoT framework that decomposes predictive uncertainty into aleatoric and epist...