US 2026/0074785 A1 · Multi-span OSNR and GSNR Prediction Using Cascaded Learning

Abstract

Disclosed is a method of cascaded learning applied to GSNR prediction using component optical amplifier, fiber, and transceiver models. The component models are measured and trained separately, before the devices are deployed into the field. Specifically, amplifier and transceiver model are trained based on measurement data, and fiber nonlinearity model are trained based on the synthesis data generated by a Gaussian Noise (GN) model. The optical link model contains all three component models and connects them as the physical order in the optical link. A small number of end-to-end measurements are used to train the optical link model to reduce the accumulated loss and adapt the model to the physical multi-span link.

Claims5

Dependent claims

A method for predicting end-to-end optical link performance in a multi-span optical network, the method comprising:

training a plurality of component-level models, each corresponding to a different optical component in the network;
constructing an optical link model by connecting the trained component-level models in the physical order of the optical components in the network;
using end-to-end link measurements to train the optical link model to adapt it to the physical multi-span link and reduce accumulated error.

Dependencies are inferred from the claim text.

Multi-span OSNR and GSNR Prediction Using Cascaded Learning

Life of this patent

Multi-span OSNR and GSNR Prediction Using Cascaded Learning

Life of this patent

Abstract

Claims5