{"title":"Line Search Computation of the Block Factor Model for Blind Multi-User Access in Wireless Communications","authors":"D. Nion, L. De Lathauwer","doi":"10.1109/SPAWC.2006.346405","DOIUrl":null,"url":null,"abstract":"In this paper, we present a technique for the blind separation of DS-CDMA signals received on an antenna array, for a multi-path propagation scenario that generates inter-symbol-interference. Our method relies on a new third-order tensor decomposition, which is a generalization of the parallel factor model. We start from the observation that the temporal, spatial and spectral diversities give a third-order tensor structure to the received data. This tensor is then decomposed in a sum of contributions, where each contribution fully characterizes one user. We also present a line search scheme that greatly improves the convergence speed of the alternating least squares algorithm previously used","PeriodicalId":414942,"journal":{"name":"2006 IEEE 7th Workshop on Signal Processing Advances in Wireless Communications","volume":"153 ","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE 7th Workshop on Signal Processing Advances in Wireless Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPAWC.2006.346405","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10
Abstract
In this paper, we present a technique for the blind separation of DS-CDMA signals received on an antenna array, for a multi-path propagation scenario that generates inter-symbol-interference. Our method relies on a new third-order tensor decomposition, which is a generalization of the parallel factor model. We start from the observation that the temporal, spatial and spectral diversities give a third-order tensor structure to the received data. This tensor is then decomposed in a sum of contributions, where each contribution fully characterizes one user. We also present a line search scheme that greatly improves the convergence speed of the alternating least squares algorithm previously used