Juan Han, Qiuju Wang, Ren Wei, Shan Tang, Jinglin Shi
{"title":"A simplified MMSE PSIC MIMO detector for turbo receivers","authors":"Juan Han, Qiuju Wang, Ren Wei, Shan Tang, Jinglin Shi","doi":"10.1109/ChinaCom.2012.6417460","DOIUrl":null,"url":null,"abstract":"In this paper, a low-complexity MIMO detector with parallel soft symbol interference cancellation and an MMSE filter, is proposed for a turbo receiver in BICM MIMO systems. The detector is utilized in non-first iterative process of Turbo receiver to suppress residual interference and noise. The complexity of the algorithm can be greatly reduced as the matrix inverse for weighting vector of MMSE is not necessary due to the approximation that the components of residual interference of PSIC and the noise are modeled as uncorrelated Gaussian random variables. Monte Carlo simulation results confirm that the proposed algorithm can achieve almost the same performance as the traditional MMSE PSIC, but with much lower complexity.","PeriodicalId":143739,"journal":{"name":"7th International Conference on Communications and Networking in China","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"7th International Conference on Communications and Networking in China","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ChinaCom.2012.6417460","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, a low-complexity MIMO detector with parallel soft symbol interference cancellation and an MMSE filter, is proposed for a turbo receiver in BICM MIMO systems. The detector is utilized in non-first iterative process of Turbo receiver to suppress residual interference and noise. The complexity of the algorithm can be greatly reduced as the matrix inverse for weighting vector of MMSE is not necessary due to the approximation that the components of residual interference of PSIC and the noise are modeled as uncorrelated Gaussian random variables. Monte Carlo simulation results confirm that the proposed algorithm can achieve almost the same performance as the traditional MMSE PSIC, but with much lower complexity.