基于混合蛙跳算法的多模盲均衡算法

doi:10.3969/j.issn.1000-1093.2015.07.017

兵工学报 ›› 2015, Vol. 36 ›› Issue (7): 1280-1287.doi: 10.3969/j.issn.1000-1093.2015.07.017

基于混合蛙跳算法的多模盲均衡算法

郭业才^1,2，张苗青¹

(1.南京信息工程大学江苏省气象探测与信息处理重点实验室江苏南京 210044;

收稿日期:2014-09-12 修回日期:2014-09-12 上线日期:2015-09-21
通讯作者: 郭业才 E-mail:guoyecai@163.com
作者简介:郭业才(1962—)男教授博士生导师
基金资助:
全国优秀博士论文作者专项资金项目(200753)；江苏省高校自然科学基金项目(13KJA510001)；高校科研成果产业化推进项目(JHB 2012-9)；江苏省高校“信息与通信工程”优势学科建设工程项目（2014年）

A Multi-modulus Blind Equalization Algorithm Based on Shuffled Frog Leaping Algorithm

GUO Ye-cai^1,2， ZHANG Miao-qing²

(1.Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of InformationScience & Technology, Nanjing 210044, Jiangsu, China;2Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment, Nanjing 210044, Jiangsu, China)

Received:2014-09-12 Revised:2014-09-12 Online:2015-09-21
Contact: GUO Ye-cai E-mail:guoyecai@163.com

摘要/Abstract

摘要： 针对常模盲均衡算法(CMA)收敛速度慢、收敛后稳态误差大且存在盲相位的现象，提出了一种基于混合蛙跳算法的多模盲均衡算法(SFLA-MMA)。它结合了智能优化算法的基本思想，将个体自身的进化及个体间的社会行为等概念引入到盲均衡技术中。该算法将多模盲均衡算法(MMA)代价函数的倒数定义为混合蛙跳算法(SFLA)的适应度函数，将青蛙群体中青蛙个体的位置向量作为MMA的初始权向量；利用SFLA的全局信息共享机制和局部深度搜索能力，在全局范围内搜索青蛙群体的最优位置向量并作为MMA的初始优化权向量。之后，通过MMA进行迭代，得到MMA的最优权向量。利用高阶多模正交振幅调制（QAM）与正交相移键控（APSK）信号对该算法进行了仿真验证。仿真结果表明，与CMA、MMA和基于粒子群算法的多模盲均衡算法(PSO-MMA)相比，SFLA-MMA在均衡高阶多模信号时收敛速度极快、稳态误差最小、输出信号星座图最清晰。

关键词: 信息处理技术, 多模算法, 混合蛙跳算法, 智能优化算法, 最优权向量

Abstract: For slow convergence speed, large steady mean square error (MSE), and blind phase of the constant modulus blind equalization algorithm (CMA), a multi-modulus blind equalization algorithm based on shuffled frog leaping algorithm (SFLA-MMA) is proposed, which combines the basic idea of intelligent optimization algorithm and introduces the individual own evolution and social behavior among individuals into the blind equalization technology. In the proposed algorithm, the reciprocal of the cost function of multi-modulus blind equalization algorithm (MMA) is defined as the fitness function of the shuffled frog leaping algorithm (SFLA), the position vector of the frog individual in the frog group is regarded as the initial weight vector of MMA. The optimum location vector of the frog groups is searched using the global information sharing mechanism and local depth search ability of SFLA, and used as the initial optimum weight vector of the MMA. The optimal weight vector of MMA is obtained by updating the weight vector of MMA . The proposed SFLA-MMA is simulated with the higher-order multi-modulus QAM and APSK signals. The simulation results show that, compared with CMA, MMA, and the multi-modulus blind equalization algorithm based on particle swarm optimization algorithm (PSO-MMA), the proposed SFLA-MMA has the fastest convergence speed, the smallest MSE, and the clearest constellations of output signals.

Key words: information processing technology, multi-modulus algorithm, shuffled frog leaping algorithm, intelligence optimization algorithm, optimal weight vector

中图分类号:

TN911.7

郭业才，张苗青. 基于混合蛙跳算法的多模盲均衡算法[J]. 兵工学报, 2015, 36(7): 1280-1287.

GUO Ye-cai， ZHANG Miao-qing. A Multi-modulus Blind Equalization Algorithm Based on Shuffled Frog Leaping Algorithm[J]. Acta Armamentarii, 2015, 36(7): 1280-1287.

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基于混合蛙跳算法的多模盲均衡算法

A Multi-modulus Blind Equalization Algorithm Based on Shuffled Frog Leaping Algorithm

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