Joint Estimation of Linear and Nonlinear Coherent Optical Fiber Signal-to-Noise Ratio

This letter proposes an estimator based on the neural network (NN) to jointly estimate the linear and nonlinear signal-to-noise ratios. The proposed NN-based estimator utilizes new input features based on the entropy extracted from the received signal. Moreover, the computational complexity of the proposed estimator is analyzed. The dataset utilized for training and testing is constructed from dual-polarization 16-ary quadrature amplitude modulation format over different system configurations of the standard single-mode fiber, such as launch power, transmission distances, and the number of wavelength division multiplexed channels. Numerical results reveal the superiority of the proposed NN-based estimator in terms of accuracy and computational complexity compared to the existing NN-based estimators in the literature.

Automated summary

This paper proposes a neural network-based estimator for jointly estimating the linear and nonlinear signal-to-noise ratios. The estimator utilizes new input features based on the entropy of the received signal. The computational complexity of the proposed estimator is also analyzed. The results demonstrate the superior accuracy and computational complexity of the proposed estimator compared to existing neural network-based estimators.