Fuzzy Neural Network Blind Equalization Algorithm Based on Signal Transformation

Abstract:

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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 and 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:

$35.00

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