Blind equalization of short burst signals based on twin support vector regressor and data-reusing method

Ling Yang, Y. Fu, Zhifen Yang, Yanyan Wei
{"title":"Blind equalization of short burst signals based on twin support vector regressor and data-reusing method","authors":"Ling Yang, Y. Fu, Zhifen Yang, Yanyan Wei","doi":"10.1109/ICCCNT.2014.6963089","DOIUrl":null,"url":null,"abstract":"In this paper, blind equalization of short burst signals is formulated with the twin support vector regressor (TSVR) framework. The proposed algorithm combine the conventional cost function of TSVR with classical error function applied to blind equalization: the Godard's error function that describes the relationship between the input signals and the desired output signals of a blind equalizer is contained in the penalty terms of TSVR, and the iterative re-weighted least square (IRWLS) algorithm is used for twin support vector regressor to achieve fast convergence. In addition, it utilizes the data-reusing method for small amounts of data samples to reach stable convergence. Simulation experiments for constant modulus signals are done to prove the feasibility and validity of the proposed algorithm.","PeriodicalId":140744,"journal":{"name":"Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCCNT.2014.6963089","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

In this paper, blind equalization of short burst signals is formulated with the twin support vector regressor (TSVR) framework. The proposed algorithm combine the conventional cost function of TSVR with classical error function applied to blind equalization: the Godard's error function that describes the relationship between the input signals and the desired output signals of a blind equalizer is contained in the penalty terms of TSVR, and the iterative re-weighted least square (IRWLS) algorithm is used for twin support vector regressor to achieve fast convergence. In addition, it utilizes the data-reusing method for small amounts of data samples to reach stable convergence. Simulation experiments for constant modulus signals are done to prove the feasibility and validity of the proposed algorithm.
基于双支持向量回归和数据重用方法的短突发信号盲均衡
本文利用双支持向量回归器(TSVR)框架建立了短突发信号的盲均衡。该算法将传统的TSVR代价函数与用于盲均衡的经典误差函数相结合,将描述盲均衡器输入信号与期望输出信号之间关系的Godard误差函数包含在TSVR的惩罚项中,采用迭代重加权最小二乘(IRWLS)算法作为双支持向量回归器实现快速收敛。此外,它还利用少量数据样本的数据重用方法来达到稳定收敛。对恒模信号进行了仿真实验,验证了该算法的可行性和有效性。
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