ML-Based QoT Estimation in Greenfield Optical Network Design with Amplifier Imperfections

ML-based QoT estimation has been recently investigated as a relevant method for the planning and operation of optical networks. While analytical Gaussian noise models offer suitable QoT estimates, their accuracy is considerably impaired if the model parameters differ substantially from those encountered in real physical networks. To alleviate this problem, ML-based QoT estimation offers better QoT estimates if the model is trained with performance parameters obtained from established field connections. The class of QoT estimation algorithms considered in this paper receives structural parameters as inputs and produces an estimated GSNR as output. We consider the case of greenfield network deployment, where a new network is implemented from scratch based on the QoT data collected from other networks. The impact of a heterogeneous training dataset with the accumulation of amplifier imperfections on the performance of the QoT estimator is investigated. We use Shapley additive explanations to quantify the contribution of particular structural parameters to ML-based GSNR estimation and reduce QoT estimation complexity. Our findings reveal that while QoT estimation for large networks remains challenging due to unlearnable random perturbations, even with training data from similarly large networks, estimation for small networks exhibits robust performance, even when trained with diverse datasets including larger networks.