Intelligent Optical Performance Monitoring Based on Intensity and Differential-Phase Features for Digital Coherent Receivers

Abstract

An intelligent optical performance monitoring scheme for simultaneous modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) monitoring is proposed and experimentally demonstrated in digital coherent receivers. The proposed scheme introduces convolutional neural network (CNN) to automatically extract and monitor modulation format and OSNR dependent features (the empirical distribution of intensity and differential-phase) which can be obtained after constant modulus algorithm (CMA) equalization. The experiment results show that 100% identification accuracies for all modulation formats (e.g. 28-GBaud PDM-QPSK/−8PSK/ −8QAM/−16QAM/−32QAM/−64QAM) are achieved at OSNR values are lower than the corresponding theoretical 20% FEC limit (BER = 2.4×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−2</sup> ). Furthermore, under the chromatic dispersion (CD) variation from 0-ps/nm to 1200-ps/nm, the root-mean- square error (RMSE) and mean absolute error (MAE) of OSNR monitoring for all modulation formats are less than 0.0896-dB and 0.0657-dB, respectively. Subsequently, the influence of frequency offset and fiber nonlinearity effect on the intelligent optical performance monitoring scheme is also analyzed. We believe that our proposed multi-parameter monitoring scheme has the potential to be applied in the next-generation intelligent elastic optical networks.