Journal of System Simulation ›› 2016, Vol. 28 ›› Issue (2): 449-454.

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Adaptive Minimum Entropy Blind Equalization Algorithm Based on Quantum Artificial Fish Swarm Optimization

Guo Yecai1,2, Wu Xing1, Huang Wei3, Wang Hui2   

  1. 1. Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science & Technology, Nanjing 210044, China;
    2. Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment, Nanjing University of Information Science & Technology, Nanjing 210044, China;
    3. School of Electrical & Information Engineering, Anhui University of Science & Technology, Huainan 232001, China;
  • Received:2014-09-28 Revised:2015-01-01 Online:2016-02-08 Published:2020-08-17

Abstract: In order to improve the equalization performance of high order inconstant modulus signals, adaptive minimum entropy super-exponential iteration blind equalization algorithm based on quantum artificial fish swarm optimization was proposed. The proposed algorithm could accelerate convergence rate via super-exponential iteration algorithm and could further decease the mean square error of the super-exponential iteration adaptive minimum entropy blind equalization algorithm via using the global optimization of the quantum artificial fish swarm algorithm designed by Schrodinger equation. The simulation results demonstrate that the proposed algorithm has fast convergence rate and lower mean square error for different higher modulation signals comparison with adaptive minimum entropy blind equalization algorithm and super-exponential iteration adaptive minimum entropy blind equalization algorithm.

Key words: blind equalization algorithm, amplitude phase shift key, convergence rate, quantum artificial fish swarm

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