{"title":"Recursive multiuser detection for DS-UWB systems","authors":"Yihai H. Zhang, Wu-Sheng Lu, T. Gulliver","doi":"10.1109/PACRIM.2005.1517344","DOIUrl":null,"url":null,"abstract":"Multiple access UWB communications has recently attracted a great deal of attention. In a multiple access UWB system, multiuser detection is desirable to effectively reduce multiple access interference (MAI). A suboptimal ML detector based on a recursive convex relaxation strategy is proposed for DS-UWB systems in this paper. This algorithm employs a recursive approach to deduce a sequence of reduced-size quadratic problems, in each of which only the most probable information bits are detected. Performance results are presented which show that the proposed detector provides near-optimal performance relative to the ML detector with considerably reduced computational complexity.","PeriodicalId":346880,"journal":{"name":"PACRIM. 2005 IEEE Pacific Rim Conference on Communications, Computers and signal Processing, 2005.","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"PACRIM. 2005 IEEE Pacific Rim Conference on Communications, Computers and signal Processing, 2005.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PACRIM.2005.1517344","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 14
Abstract
Multiple access UWB communications has recently attracted a great deal of attention. In a multiple access UWB system, multiuser detection is desirable to effectively reduce multiple access interference (MAI). A suboptimal ML detector based on a recursive convex relaxation strategy is proposed for DS-UWB systems in this paper. This algorithm employs a recursive approach to deduce a sequence of reduced-size quadratic problems, in each of which only the most probable information bits are detected. Performance results are presented which show that the proposed detector provides near-optimal performance relative to the ML detector with considerably reduced computational complexity.