Spread Spectrum Code Estimation by Artificial Fish Swarm Algorithm

M. Jiang, Yong Wang, F. Rubio, D. Yuan
{"title":"Spread Spectrum Code Estimation by Artificial Fish Swarm Algorithm","authors":"M. Jiang, Yong Wang, F. Rubio, D. Yuan","doi":"10.1109/WISP.2007.4447587","DOIUrl":null,"url":null,"abstract":"A new estimation method by the swarm intelligent optimization algorithm is presented to recover the transmitted data bits and code of spread spectrum signal over additive white Gaussian noise channel, while the receiver has no knowledge of the transmitter spreading sequence, only knows the length of spreading sequence. The presented estimation method by Artificial Fish Swarm Algorithm (AFSA) is insensitive to initial values, has a strong robustness, and has the faster convergence speed and better estimation precision than the estimation method by Genetic Algorithm (GA) and the estimation method by Particle Swarm Optimization (PSO). The results show that the method can obtain the optimal or sub-optimal estimation of spreading code, even when the signal power is below the noise power.","PeriodicalId":164902,"journal":{"name":"2007 IEEE International Symposium on Intelligent Signal Processing","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"39","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE International Symposium on Intelligent Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WISP.2007.4447587","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 39

Abstract

A new estimation method by the swarm intelligent optimization algorithm is presented to recover the transmitted data bits and code of spread spectrum signal over additive white Gaussian noise channel, while the receiver has no knowledge of the transmitter spreading sequence, only knows the length of spreading sequence. The presented estimation method by Artificial Fish Swarm Algorithm (AFSA) is insensitive to initial values, has a strong robustness, and has the faster convergence speed and better estimation precision than the estimation method by Genetic Algorithm (GA) and the estimation method by Particle Swarm Optimization (PSO). The results show that the method can obtain the optimal or sub-optimal estimation of spreading code, even when the signal power is below the noise power.
基于人工鱼群算法的扩频码估计
在接收端不知道发送端扩频序列,只知道扩频序列长度的情况下,提出了一种利用群智能优化算法对加性高斯白噪声信道上的扩频信号进行传输数据位和码的估计方法。本文提出的人工鱼群算法(AFSA)估计方法对初始值不敏感,具有较强的鲁棒性,与遗传算法(GA)和粒子群优化(PSO)估计方法相比,具有更快的收敛速度和更高的估计精度。结果表明,即使信号功率低于噪声功率,该方法也能得到扩频码的最优或次最优估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信