GNPy is a tool or software used for Quality-of-Transmission (QoT) estimation in optical networks. It is based on the Generalized Gaussian Noise (GGN) model, designed to predict the signal quality in fiber-optic communication systems. Specifically, GNPy is being enhanced and experimentally validated for C+L multiband transmission (which covers a broad range of frequencies in optical communication).
We implement a cascaded learning framework leveraging three different EDFA and fiber component models for OSNR and GSNR prediction, achieving MAEs of 0.20 and 0.14 dBover a 5-span network under dynamic channel loading.
We propose building a spectrally resolved QoT Digital Twin for optical network domains where models and telemetry are unavailable, by probing transmission on a singlespectral slot, using GNPy, and demonstrating accurate experimental results.
The GNPy quality-of-transmission estimator has undergone improvements and rigorous experimental validation in a C+L multiband transmission scenario. This includes the incorporation of a disaggregated generalized Gaussian noise model, along with advanced modeling of amplifiers and transceivers. The recently proposed implementation demonstrates notable enhancements, offering highly accurate GSNR predictions on commercial C+L-band equipment while significantly reducing computation time.
A control architecture for a partially disaggregated optical network is proposed using a GNPy-based digital twin for QoT estimation. The proposed implementation enables soft failure mitigation by autonomously adjusting the amplifier working points.
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