Jun Tong, Qinghua Guo, J. Xi, Yanguang Yu, P. Schreier
{"title":"正则格约简辅助有序逐次干扰消除MIMO检测","authors":"Jun Tong, Qinghua Guo, J. Xi, Yanguang Yu, P. Schreier","doi":"10.1109/SSP.2018.8450791","DOIUrl":null,"url":null,"abstract":"Lattice reduction-aided ordered successive interference cancellation (LRA-OSIC) detection is capable of achieving optimal diversity orders for multiple-input multiple-output (MIMO) communications. When the number of antennas is large, however, there can still be a significant gap between the performance achievable with the LRA-OSIC detector and the maximum likelihood detector (MLD). This paper introduces a regularization approach to enhance the performance of LRA-OSIC detectors. Multiple approximate models for the same MIMO channel are generated and a standard LRA-OSIC detector is then constructed for each model. The best detector is determined for each instantaneous received symbol, using a residual-based method. The search can be terminated using a stopping criterion. Simulation results show that significant performance enhancements can be achieved by the proposed design at only a moderate increase of complexity1.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Regularized Lattice Reduction-Aided Ordered Successive Interference Cancellation for MIMO Detection\",\"authors\":\"Jun Tong, Qinghua Guo, J. Xi, Yanguang Yu, P. Schreier\",\"doi\":\"10.1109/SSP.2018.8450791\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Lattice reduction-aided ordered successive interference cancellation (LRA-OSIC) detection is capable of achieving optimal diversity orders for multiple-input multiple-output (MIMO) communications. When the number of antennas is large, however, there can still be a significant gap between the performance achievable with the LRA-OSIC detector and the maximum likelihood detector (MLD). This paper introduces a regularization approach to enhance the performance of LRA-OSIC detectors. Multiple approximate models for the same MIMO channel are generated and a standard LRA-OSIC detector is then constructed for each model. The best detector is determined for each instantaneous received symbol, using a residual-based method. The search can be terminated using a stopping criterion. Simulation results show that significant performance enhancements can be achieved by the proposed design at only a moderate increase of complexity1.\",\"PeriodicalId\":330528,\"journal\":{\"name\":\"2018 IEEE Statistical Signal Processing Workshop (SSP)\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE Statistical Signal Processing Workshop (SSP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SSP.2018.8450791\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE Statistical Signal Processing Workshop (SSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSP.2018.8450791","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Regularized Lattice Reduction-Aided Ordered Successive Interference Cancellation for MIMO Detection
Lattice reduction-aided ordered successive interference cancellation (LRA-OSIC) detection is capable of achieving optimal diversity orders for multiple-input multiple-output (MIMO) communications. When the number of antennas is large, however, there can still be a significant gap between the performance achievable with the LRA-OSIC detector and the maximum likelihood detector (MLD). This paper introduces a regularization approach to enhance the performance of LRA-OSIC detectors. Multiple approximate models for the same MIMO channel are generated and a standard LRA-OSIC detector is then constructed for each model. The best detector is determined for each instantaneous received symbol, using a residual-based method. The search can be terminated using a stopping criterion. Simulation results show that significant performance enhancements can be achieved by the proposed design at only a moderate increase of complexity1.