[1]郭业才,吴华鹏.双蝙蝠群智能优化的多模盲均衡算法[J].智能系统学报编辑部,2015,10(5):755-761.[doi:10.11992/tis.201407031]
 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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双蝙蝠群智能优化的多模盲均衡算法(/HTML)
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《智能系统学报》编辑部[ISSN:1673-4785/CN:23-1538/TP]

卷:
第10卷
期数:
2015年5期
页码:
755-761
栏目:
出版日期:
2015-10-25

文章信息/Info

Title:
Multi-modulus blind equalization algorithm based on double bat swarms intelligent optimization
作者:
郭业才12 吴华鹏2
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;
2. 江苏省大气环境与装备技术协同创新中心, 江苏 南京 210044
Author(s):
GUO Yecai12 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
关键词:
常模盲均衡算法多模盲均衡算法蝙蝠算法全局最优位置最优权向量
Keywords:
constant modulus algorithm (CMA)multi-modulus blind equalization algorithm (MMA)bat algorithm (BA)global optimal positionoptimal weight vector
分类号:
TN911;TP182
DOI:
10.11992/tis.201407031
文献标志码:
A
摘要:
针对常模盲均衡算法(CMA)均衡多模QAM信号收敛速度慢、剩余均方误差大的缺陷,提出了一种基于双蝙蝠群智能优化的多模盲均衡算法(DBSIO-MMA)。该算法将2个蝙蝠群独立全局寻优得到的一组最优位置向量分别作为多模盲均衡算法(MMA)初始化最优权向量的实部与虚部,以此提高收敛速度并减小剩余均方误差。仿真结果表明,蝙蝠算法(BA)全局搜索成功率高、收敛速度快的特点在DBSIO-MMA中得到很好地体现。与CMA、MMA、粒子群多模盲均衡算法(PSO-MMA)、单蝙蝠群多模盲均衡算法(BA-MMA)相比,DBSIO-MMA具有更快的收敛速度和更小的均方误差。
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.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2014-07-21;改回日期:。
基金项目:江苏省高校自然科学基金重大资助项目(13KJA510001);高校科研成果产业化推进工程资助项目(JHB2012-9);全国优秀博士论文作者专项资金资助项目(200753);江苏省高校“信息与通信工程”优势学科建设工程资助项目(2014).
作者简介:郭业才,男,1962年生,博士、教授、博士生导师,主要研究方向为通信系统与信号处理、智能计算与优化、大气声学与海洋声息学、图像处理技术等。主持和参与国家及省部级项目20余项,获得省级教学与科研成果奖5项,省级鉴定成果3项,授权发明专利20余项,授权实用新型专利20余项,软件著作权30项。发表学术论文200余篇,其中被SCI、EI、ISTP收录150余篇,出版专著4部;吴华鹏,男,1991年生,硕士研究生,主要研究方向为通信信号处理。
通讯作者:郭业才.E-mail:guo-yecai@163.com.
更新日期/Last Update: 2015-11-16