Neural Network Equalizer
In this paper, we view equalization as a multi-class classification problem and use neural networks to detect binary signals in the presence of noise and interference. In particular, we compare the performance of a recently published training algorithm, a multi-gradient, with that of the conventional back-propagation. Then, we apply a feature extraction to obtain more efficient neural networks. Experiments show that neural network equalizers which view equalization as multi-class problems provide significantly improved performance compared to the conventional LMS algorithm while the decision boundary feature extraction method significantly reduces the complexity of the network.
KeywordsClassification Accuracy Less Mean Square Decision Boundary Less Mean Square Algorithm Intersymbol Interference
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