Paper Title:
Fuzzy Neural Network Blind Equalization Algorithm Based on Signal Transformation
  Abstract

To recover QAM signals at the receiver of blind equalizer, a Fuzzy C-means clustering Neural Network Blind Equalization Algorithm based on Signal Transformation (ST-FNN-BEA) is proposed. The proposed algorithm uses signal transformation method to debase the computational complexity of equalizer input signals and speed up the convergence rate, and makes use of fuzzy c-means clustering algorithm dividing the equalizer input signals into each cluster center with different membership values to improve the equalization performance. The proposed ST-FNN-BEA outperforms Neural Network Blind Equalization Algorithm (NN-BEA) and Neural Network Blind Equalization Algorithm based on Signal Transformation (ST-NN-BEA) in improving convergence rates and reducing mean square error. The performance of ST-FNN-BEA is proved by the computer simulation with underwater acoustic channels.

  Info
Periodical
Edited by
Ran Chen
Pages
4146-4150
DOI
10.4028/www.scientific.net/AMM.44-47.4146
Citation
Y. C. Guo, Z. X. Liu, "Fuzzy Neural Network Blind Equalization Algorithm Based on Signal Transformation", Applied Mechanics and Materials, Vols. 44-47, pp. 4146-4150, 2011
Online since
December 2010
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Price
$32.00
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