RESUMEN
Optical signal-to-noise ratio (OSNR) monitoring is one of the core tasks of advanced optical performance monitoring (OPM) technology, which plays an essential role in future intelligent optical communication networks. In contrast to many regression-based methods, we convert the continuous OSNR monitoring into a classification problem by restricting the outputs of the neural network-based classifier to discrete OSNR intervals. We also use a low-bandwidth coherent receiver for obtaining the time domain samples and a long short-term memory (LSTM) neural network as the chromatic dispersion-resistant classifier. The proposed scheme is cost efficient and compatible with our previously proposed multi-purpose OPM platform. Both simulation and experimental verification show that the proposed OSNR monitoring technique achieves high classification accuracy and robustness with low computational complexity.
