Estimating Quality of Transmission in a Live Production Network using Machine Learning
arXiv:2112.04031 · doi:10.1364/OFC.2021.Tu1G.2
Abstract
We demonstrate QoT estimation in a live network utilizing neural networks trained on synthetic data spanning a large parameter space. The ML-model predicts the measured lightpath performance with <0.5dB SNR error over a wide configuration range.
The work has been partially funded by the German Ministry of Education and Research in the project OptiCON (contract #16KIS0989K)