OSNR monitoring based on a low-bandwidth coherent receiver and LSTM classifier

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. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Automated summary

The study proposes a method to classify optical signal-to-noise ratio monitoring using a neural network-based classifier, demonstrating successful results in experiments.