Linear equalization via factor graphs

R. Drost, A. Singer
{"title":"Linear equalization via factor graphs","authors":"R. Drost, A. Singer","doi":"10.1109/ISIT.2004.1365169","DOIUrl":null,"url":null,"abstract":"This paper apply the factor graph framework to the techniques of linear equalization and decision feedback equalization to obtain a new class of low complexity equalization algorithms. The estimation of Gaussian processes has been studied in previous work, and the application of factor graphs to this problem is a recent extension. Here it uses a factor graph model for the specific estimation problem of equalization and use the sum-product algorithm to obtain the desired estimate. The reduced complexity message passing update equations are derived and detail the complexity of the resulting algorithms.","PeriodicalId":269907,"journal":{"name":"International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings.","volume":"9 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISIT.2004.1365169","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

This paper apply the factor graph framework to the techniques of linear equalization and decision feedback equalization to obtain a new class of low complexity equalization algorithms. The estimation of Gaussian processes has been studied in previous work, and the application of factor graphs to this problem is a recent extension. Here it uses a factor graph model for the specific estimation problem of equalization and use the sum-product algorithm to obtain the desired estimate. The reduced complexity message passing update equations are derived and detail the complexity of the resulting algorithms.
通过因子图实现线性均衡
本文将因子图框架应用于线性均衡和决策反馈均衡技术,得到了一类新的低复杂度均衡算法。高斯过程的估计在以前的工作中已经得到了研究,而因子图在这个问题中的应用是最近的一个推广。这里使用因子图模型来解决均衡化的具体估计问题,并使用和积算法来获得期望的估计。推导了降低复杂度的消息传递更新方程,并详细说明了所得到的算法的复杂度。
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