Going Deeper into OSNR Estimation with CNN

Abstract

As optical performance monitoring (OPM) requires accurate and robust solutions to tackle the increasing dynamic and complicated optical network architectures, we experimentally demonstrate an end-to-end optical signal-to-noise (OSNR) estimation method based on the convolutional neural network (CNN), named OptInception. The design principles of the proposed scheme are specified. The idea behind the combination of the Inception module and finite impulse response (FIR) filter is elaborated as well. We experimentally evaluate the mean absolute error (MAE) and root-mean-squared error (RMSE) of the OSNR monitored in PDM-QPSK and PDM-16QAM signals under various symbol rates. The results suggest that the MAE reaches as low as 0.125 dB and RMSE is 0.246 dB in general. OptInception is also proved to be insensitive to the symbol rate, modulation format, and chromatic dispersion. The investigation of kernels in CNN indicates that the proposed scheme helps convolutional layers learn much more than a lowpass filter or bandpass filter. Finally, a comparison in performance and complexity presents the advantages of OptInception.

Joint modulation format recognition and optical performance monitoring for efficient fiber-optic communication links using ensemble deep transfer learning

“…Successfully, ML implementations are also performed to transform future intelligent optical networks. 8,10 Results from a study [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33] prove the advantages of using MLbased methods that can be used for futuristic predictions. This paradigm shift results from the various possible advantages provided by ML.…”

“…The true positive (TP), true negative (TN), false positive (FP), and false negative (FN) values are calculated from the confusion matrix. The metrics and their derivative formulas, which are used in the comparison of the imbalance metrics of the proposed algorithm, are given in the following equations: [18][19][20][21][22]35,36 E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 0 9 ; 1 1 6 ; 5 5 5…”

“…Recently, CNN techniques have gained momentum for successful application to image-based pattern-recognition tasks 18 – 32 in optical communication applications. Compared with other prediction models, CNN has gained importance in recent times due to its ability to extract features directly from raw images, automatically yielding higher efficiency and accuracy.…”

“…Tanimura et al 22 . proposed a CNN-based OSNR estimator in coherently received optical networks and 21 estimated OSNR using an inception module and finite impulse response filter combination and proposing the OptInception CNN method. In Refs.…”

Intelligent Matching of the Control Voltage of Delay Line Interferometers for Differential Phase Shift Keying-Modulated Optical Signals

“…In recent years, a lot of research has been conducted on neural networks and artificial intelligence, with the goal of achieving excellent performance on nonlinear modeling and prediction problems. Neural networks are also widely applied in optical communication networks for physical layer solutions [19][20][21][22][23][24]. In this paper, we use an FNN to quickly locate the optimal DLI control voltage based on an FNN.…”