Accurate OSNR monitoring based on data-augmentation-assisted DNN with a small-scale dataset.

Abstract

Deep neural networks (DNNs) have been successfully applied for accurate optical signal-to-noise ratio (OSNR) monitoring. However, the performance of OSNR monitoring substantially degrades when a mega dataset is inaccessible. Here, we demonstrate an accurate OSNR monitoring scheme based on a data-augmentation (DA)-assisted DNN with a small-scale dataset. When a 20 GBaud quadrature phase shift keying (QPSK) signal is transmitted over 400 to 2600 km standard single-mode fiber (SSMF) with an OSNR range from 8 to 14 dB, we experimentally evaluate the minimum dataset size to secure a mean absolute error (MAE) of OSNR monitoring less than 1 dB. The DA-assisted scheme only requires 50% of the raw data, in comparison with the traditional DNN scheme. Thus, the DA-assisted DNN scheme is promising for field-trial accurate OSNR monitoring, especially when the collection of mega datasets is inconvenient.