A machine learning-based estimation method for fiber nonlinear noise-to-signal ratio is proposed, which does not require channel state information.
A machine learning-based estimation method for fiber nonlinear noise-to-signal ratio is proposed, which does not require channel state information. The ...
A machine learning-based estimation method for fiber nonlinear noise-to-signal ratio is proposed, which does not require channel state information.
We propose a machine learning based method to estimate the proportion of inter-channel and intra-channel nonlinear effects. The model is tested with simulations ...
Fiber Nonlinear Noise-to-Signal Ratio Estimation by Machine Learning. K. Zhang, Y. Fan, T. Ye, Z. Tao, S. Oda, T. Tanimura, Y. Akiyama, and T. Hoshida.
This paper estimates the linear and nonlinear signal-to-noise ratio (SNR) from the received signal by obtaining features of two distinct effects: nonlinear ...
The average RMSE of NL noise power estimation can reach 0.25 dB. The results show that the monitoring scheme is robust to the increase of fiber length, and the ...
Five ML models have been trained using features extracted from the nonlinear phase noise generated by signal-signal interaction between WDM channels. Model ...
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Jun 1, 2021 · In this paper, a convolutional neural network (CNN) based algorithm is proposed for the estimation of nonlinear phase shift (NLPS) in coherent optical ...
Abstract—We propose a method to estimate linear and nonlin- ear signal-to-noise ratio (SNR) by using normalized correlations.