Neural Network Equalizer

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• 作者：Chulhee Lee ; Jinwook Go ; Byungjoon Baek ; Hyunsoo Choi
• 刊名：Lecture Notes in Computer Science
• 出版年：2006
• 出版时间：2006
• 年：2006
• 卷：4113
• 期：1
• 页码：204-215
• 全文大小：1,956 KB

文摘

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.