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.

• Periodical: Applied Mechanics and Materials (Volumes 44-47)
• Pages: 4146-4150
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.44-47.4146
• Online since: December 2010
• Authors: Ye Cai Guo, Zheng Xin Liu
• Keywords: Fuzzy C-Means Clustering Algorithm, Fuzzy Neural Network (FNN), Signal Transformation, Underwater Acoustic Channel

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