Scalable ML models and cascaded learning for efficient multi-span OSNR and GSNR prediction

Scalable and efficient methods for predicting optical transmission performance using machine learning (ML) and cascaded learning (CL) are investigated in metro multi-span optical networks. We extend the CL framework beyond multi-span power spectrum prediction to enable accurate estimation of optical signal-to-noise (OSNR) and generalized signal-to-noise (GSNR) under dynamic channel loading, integrating cascaded component models for end-to-end (E2E) optical link performance prediction. To mitigate error accumulation inherent in cascaded predictions, additional E2E optical link measurements and models are incorporated. To ensure scalability, component-level models for erbium-doped fiber amplifier (EDFA) gain and noise figure are pre-trained prior to deployment. We demonstrate that the data collection effort for pre-training the EDFA model can be reduced to only 5.5% of the original training set through transfer learning. Furthermore, Gaussian noise (GN)-based analytical models are leveraged to assist in the training of ML-based models for fiber loss and nonlinear impairments. The proposed approach is evaluated under four distinct optical link configurations. On a five-span system comprising six EDFAs, the method achieves a mean absolute error of 0.22 and 0.13 dB for OSNR prediction of background channels and GSNR prediction for 400 GbE channels, respectively.