Neural network equalizer

Authors:
Chulhee Lee;Jinwook Go;Byungjoon Baek;Hyunsoo Choi
Affiliations:
Dept. Electrical and Electronic Engineering, Yonsei University, Seoul, South Korea;Dept. Electrical and Electronic Engineering, Yonsei University, Seoul, South Korea;Dept. Electrical and Electronic Engineering, Yonsei University, Seoul, South Korea;Dept. Electrical and Electronic Engineering, Yonsei University, Seoul, South Korea
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Year:
2006

Citing 5
Cited 0

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Abstract

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.