Rocco D’Ingillo works at Politecnico di Torino.
We combine few-shot learning and grey-box modeling for EDFAs in optical lines, training a single EDFA model on 500 spectral loads and transferring it to other EDFAs using 4-8 samples, maintaining low OSNR prediction error.
In Optical Multiplex Section (OMS) control and optimization framework, end-to-end (Global) and span-by-span (Local) DNN gray-box strategies are compared in terms of scalability and accuracy of the output signal and noise power predictions. Experimental measurements are carried out in OMSs with increasing number of spans.
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.
We propose a deep learning algorithm trained on varied spectral loads and EDFA working points to generate a digital twin of an optical line system able to optimize line control and to enhance OSNR predictions.
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