{"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.