[1]GUO Yecai,WU Huapeng.Multi-modulus blind equalization algorithm based on double bat swarms intelligent optimization[J].CAAI Transactions on Intelligent Systems,2015,10(5):755-761.[doi:10.11992/tis.201407031]
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Multi-modulus blind equalization algorithm based on double bat swarms intelligent optimization

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 5 Page number: 755-761 Column: 学术论文—机器学习 Public date: 2015-10-25
Title:
Multi-modulus blind equalization algorithm based on double bat swarms intelligent optimization
Author(s):
GUO Yecai1; 2;  WU Huapeng2
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 Technol-ogy(CICAEET), Nanjing 210044, China
Keywords:
constant modulus algorithm (CMA); multi-modulus blind equalization algorithm (MMA); bat algorithm (BA); global optimal position; optimal weight vector
CLC:
TN911;TP182
DOI:
10.11992/tis.201407031
Abstract:
Aiming at the defects of the large surplus mean square error and slow convergence speed in equalizing multi-modulus QAM signals by utilizing constant modulus algorithm (CMA), a multi-modulus blind equalization algorithm based on double bat swarms intelligent optimization (DBSIO-MMA) is proposed. In the algorithm, a group of optimal position vectors attained by independent global optimization of two bat swarms are respectively taken as the real and imaginary parts of the initialized optimal weight vector, so as to improve convergence speed and reduce surplus mean square error. The simulation results show that the features of fast convergence speed and high success rate of the bat algorithm (BA) in global search are fully reflected in the proposed algorithm. Compared with the CMA, multi-modulus blind equalization algorithm (MMA), particle swarm optimization based MMA (PSO-MMA) and bat swarms intelligent optimization based MMA (BA-MMA), the proposed algorithm has faster convergence speed and smaller mean square error.
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