{"title":"A Novel Near Maximum Likelihood Detection Scheme for Wireless MIMO Systems","authors":"Jiming Chen, W. Mow, Shaoqian Li","doi":"10.1109/ITW2.2006.323699","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a novel near maximum likelihood detection (MLD) scheme for wireless multiple-input multiple-output (MIMO) systems. The scheme is to reduce the dimension by first using the minimum mean square error (MMSE) detection to fix symbols with high signal to interference plus noise ratio (SINR), and then calculate the bit log-likelihood ratio (LLR) for the initial MMSE estimate, construct a list of reliability-based candidate vectors using the corresponding LLR, and return the most likely bit vector among all visited options. Simulation results show that the proposed method can approach identical performance to that of MLD but with a significant reduction in complexity","PeriodicalId":299513,"journal":{"name":"2006 IEEE Information Theory Workshop - ITW '06 Chengdu","volume":"59 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE Information Theory Workshop - ITW '06 Chengdu","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITW2.2006.323699","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
In this paper, we propose a novel near maximum likelihood detection (MLD) scheme for wireless multiple-input multiple-output (MIMO) systems. The scheme is to reduce the dimension by first using the minimum mean square error (MMSE) detection to fix symbols with high signal to interference plus noise ratio (SINR), and then calculate the bit log-likelihood ratio (LLR) for the initial MMSE estimate, construct a list of reliability-based candidate vectors using the corresponding LLR, and return the most likely bit vector among all visited options. Simulation results show that the proposed method can approach identical performance to that of MLD but with a significant reduction in complexity