Polarization Tracking and Optical Nonlinearity Compensation Using Artificial Neural Networks

Kurokawa, Yuichiro; Kyono, Takeru; Nakamura, Moriya

doi:10.1109/oecc48412.2020.9273736

Cited by 3 publications

(1 citation statement)

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“…However, these methods need an enormous amount of calculations, which increases the power consumption and the delay time at the receiver. On the other hand, artificial neural network (ANN)-based nonlinear equalizers are attracting attention because of their lower computational complex-ity [3,4,5,6,7]. In recent years, the rectified linear unit (ReLU) has often been employed as an activation function of the ANN-unit instead of the conventional sigmoid function in deep neural networks (DNNs) used for, e.g., speech recognition and image recognition [8,9].…”

Section: Introductionmentioning

confidence: 99%

Activation functions of artificial-neural-network-based nonlinear equalizers for optical nonlinearity compensation

et al. 2021

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We investigated the performance of artificial neural network (ANN)-based nonlinear equalizers for optical nonlinearity compensation by comparing activation functions, including a sigmoid function, ReLU, and Leaky ReLU. We compared the learning speeds and compensation performances by evaluating the resulting error vector magnitudes of the compensated signals. The performance was investigated using simulated 100-km optical fiber transmission of 10-GSymbol/s 16QAM signals. When the number of hidden-layer units in the ANN was small, the sigmoid function showed better performance in learning speed than ReLU and Leaky ReLU. This point is important because the number of ANN units has to be reduced in order to improve the computational complexity of the ANN-based nonlinear equalizer.

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