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Orthogonal wavelet transform weighted multi-modulus blind equalization algorithm based on quantum particle swarm optimization

Guo Ye-Cai Hu Ling-Ling Ding Rui

Orthogonal wavelet transform weighted multi-modulus blind equalization algorithm based on quantum particle swarm optimization

Guo Ye-Cai, Hu Ling-Ling, Ding Rui
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  • Received Date:  17 April 2011
  • Accepted Date:  22 May 2011
  • Published Online:  05 March 2012

Orthogonal wavelet transform weighted multi-modulus blind equalization algorithm based on quantum particle swarm optimization

  • 1. College of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China;
  • 2. School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China
Fund Project:  Project supported by the Special Fund Projects of National Excellent Doctoral Dissertation of China (Grant No. 200753), Natural Science Foundation of Higher Education Institution of Anhui Province, China (Grant No. KJ2010-A096), Natural Science Foundation of Higher Education Institution of Jiangsu Province , China (Grant No. 08KJB510010), “the Peak of Six Major Talents” Cultivate Projects of Jiangsu Province, China (Grant No. 2008026), Jiangsu Provincial Natural Science Foundation, China (Grant No. BK2009410), and the Jiangsu Preponderant Discipline “Sensing Networks and Modern Meteorological Equipment” of China.

Abstract: When constant modulus blind equalization algorithm (CMA) is used to equalize high-order QAM signals, there occur the defects of the slow convergence rate and big steady mean square error. In order to overcome these disadvantages, orthogonal wavelet transform weighted multi-modulus blind equalization algorithm based on the quantum particle swarm optimization (QPSO-WTWMMA) is proposed. In this proposed algorithm, quantum particle swarm optimization algorithm and orthogonal wavelet transform are combined into weighted multi-modulus blind equalization algorithm (WMMA) according to the feature of higher-order QAM signal constellations. Accordingly, the equalizer weight vector can be optimized by QPSO algorithm, the autocorrelation of the input signals can be reduced via using orthogonal wavelet transform, and WMMA is used to choose appropriate error models to match QAM constellations. The theoretical analyses and the computer simulations in underwater acoustic channels indicate that the proposed algorithm can obtain the fastest convergence rate and the smallest steady mean square error in equalizing high-order QAM signals. So, the proposed algorithm has important reference value for the underwater acoustic communications.

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