Feature extraction for neural network equalizers trained with multi-gradient

In this paper, we view equalization as a multi-class classification problem and use neural networks for classification. In particular, we use a recently published training algorithm, multi-gradient, to train neural networks. Then, we apply a feature extraction method to obtain more efficient neural networks. Experiments show that the neural network equalizers which view equalization as multi-class problems provide significantly improved performances compared to neural network equalizers trained by the conventional LMS algorithm while the feature extraction method significantly reduces the complexity of the neural network equalizers.