Learned Signal-to-Noise Ratio Estimation in Optical Fiber Communication Links

This paper proposes a signal-to-noise ratio (SNR)
estimator based on recurrent neural network (RNN) in optical
fiber communication links. The proposed estimator jointly estimates the linear and nonlinear components of the SNR. The input features of the proposed estimator are carefully designed based on a combination of the lower quartile and entropy extracted from the This paper proposes a signal-to-noise ratio (SNR) estimator based on recurrent neural network (RNN) in optical
fiber communication links. The proposed estimator jointly estimates the linear and nonlinear components of the SNR. The input features of the proposed estimator are carefully designed based on a combination of the lower quartile and entropy extracted from the received signal. The proposed input features do not require knowledge of the transmitted symbols. In the proposed SNR estimator, three different RNN models are investigated, namely simple RNN, gated recurrent units, and long short-term memory. The overall computational complexity of the three models of the proposed estimator, including the feature extraction and RNN structures, are analyzed. Numerical results show that the three models of the proposed estimator provide a trade-off between the complexity of the RNN structure and estimation accuracy. Furthermore, the proposed estimator achieves a better SNR estimation accuracy and reduces the overall computational complexity compared to the literature.