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.

Duke Scholars

Author Tingjun Chen Electrical and Computer Engineering