A Novel Nonlinear Noise Power Estimation Method Based on Error Vector Correlation Function Using Artificial Neural Networks For Coherent Optical Fiber Transmission Systems

Abstract

In this paper, we propose a promising nonlinear noise power estimation method based on correlation functions using artificial neural networks (ANN), which is robust against both amplifier spontaneous emission noise and the symbol patterns. Error vector correlation (EVC), together with the amplitude noise correlation (ANC) and the phase noise correlation (PNC), is used as the input of ANN in the proposed method. 378 cases of 224 Gb/s polarization-multiplexed 16-quadrature amplitude modulated (PM-16-QAM) signal transmitted over a wide range of conditions by varying launch power, OSNR and transmission distance are used to train and test the ANN. With the launch power varying from 0 to 8 dBm and the transmission distance as long as 2400 km, the results of tested samples demonstrate that the maximum absolute deviation (MAD), mean absolute error (MAE) and root mean square error (RMSE) of the estimated nonlinear noise power are 0.65 dB, 0.20 dB and 0.27 dB, respectively. In order to verify the independence of symbol patterns, 150 new cases with 30 pseudo-random binary sequence (PRBS) seeds are used to test the trained ANN. The results show that the MAE, MAD and RMSE of estimated nonlinear noise power are 0.86 dB, 0.25 dB and 0.33 dB respectively, meaning that the trained ANN is valid even if the test samples are not covered by the trained process. The results in this work verified that the ANN with EVC, ANC and PNC as input can make the trained model feasible to accurately estimate the nonlinear noise power in high speed optical coherent communication systems.

Low-complexity EVM estimation based on artificial neural networks for coherent optical systems

“…ANN-based transfer learning models are proposed for quality of transmission prediction feasible with different MFs [15]. Recently, ANN architecture has been utilized in order to predict the noise power employing correlation functions, which are resistant to symbol patterns [16]. Furthermore, a novel ANN-based technique has also been suggested to reliably predict OSNR and MFs using kurtosis, showcasing high accuracy across wide OSNR ranges for QPSK, 8QAM, 16QAM, and 64QAM [17].…”