Toward Better QoT Estimation: An ML Architecture With Link-Level Embedding Layers

Abstract

Machine learning (ML) is emerging as a promising tool for estimating the Quality of Transmission (QoT) in optical networks, especially for unestablished lightpaths where traditional methods are limited. However, inaccuracies in ML-based QoT predictions—typically expressed in terms of generalized signal-to-noise ratio (GSNR)—can significantly affect network operation. Overestimation may lead to retransmissions due to overly aggressive modulation format choices, while underestimation results in underutilized spectral resources. To address this, we propose a novel ML architecture that incorporates an embedding layer for link-level features alongside path- and service-level inputs. Using data generated from an accurate analytical model, we show that our approach reduces prediction error by up to 34% compared to standard architectures. Simulated deployment scenarios further demonstrate operational benefits, with a 15.9% decrease in incorrect and a 34.8% reduction in overly conservative modulation format selections.