{"title":"基于landweber方法的大规模MIMO系统上行链路低复杂度检测","authors":"Wence Zhang, Xu Bao, Jisheng Dai","doi":"10.23919/EUSIPCO.2017.8081332","DOIUrl":null,"url":null,"abstract":"In this paper, we present low-complexity uplink detection algorithms in Massive MIMO systems. We treat the uplink detection as an ill-posed problem and adopt Landweber Method to solve it. In order to reduce the computational complexity and increase the convergence rate, we optimize the relax factor and propose improved Landweber Method with optimal relax factor (ILM-O) algorithm. We also try to reduce the order of Landweber Method by introducing a set of coefficients and propose reduced order Landweber Method (ROLM) algorithm. A analysis on the convergence and the complexity is provided. Numerical results show that the proposed algorithms outperform the existing algorithm significantly when the system scale is large.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"330 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Low-complexity detection based on landweber method in the uplink of Massive MIMO systems\",\"authors\":\"Wence Zhang, Xu Bao, Jisheng Dai\",\"doi\":\"10.23919/EUSIPCO.2017.8081332\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present low-complexity uplink detection algorithms in Massive MIMO systems. We treat the uplink detection as an ill-posed problem and adopt Landweber Method to solve it. In order to reduce the computational complexity and increase the convergence rate, we optimize the relax factor and propose improved Landweber Method with optimal relax factor (ILM-O) algorithm. We also try to reduce the order of Landweber Method by introducing a set of coefficients and propose reduced order Landweber Method (ROLM) algorithm. A analysis on the convergence and the complexity is provided. Numerical results show that the proposed algorithms outperform the existing algorithm significantly when the system scale is large.\",\"PeriodicalId\":346811,\"journal\":{\"name\":\"2017 25th European Signal Processing Conference (EUSIPCO)\",\"volume\":\"330 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 25th European Signal Processing Conference (EUSIPCO)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/EUSIPCO.2017.8081332\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 25th European Signal Processing Conference (EUSIPCO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/EUSIPCO.2017.8081332","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Low-complexity detection based on landweber method in the uplink of Massive MIMO systems
In this paper, we present low-complexity uplink detection algorithms in Massive MIMO systems. We treat the uplink detection as an ill-posed problem and adopt Landweber Method to solve it. In order to reduce the computational complexity and increase the convergence rate, we optimize the relax factor and propose improved Landweber Method with optimal relax factor (ILM-O) algorithm. We also try to reduce the order of Landweber Method by introducing a set of coefficients and propose reduced order Landweber Method (ROLM) algorithm. A analysis on the convergence and the complexity is provided. Numerical results show that the proposed algorithms outperform the existing algorithm significantly when the system scale is large.