{"title":"大规模MIMO系统中基于稀疏约束的联合有源器件识别与符号检测","authors":"Ganapati Hegde, M. Pesavento, M. Pfetsch","doi":"10.23919/EUSIPCO.2017.8081298","DOIUrl":null,"url":null,"abstract":"In this paper, we consider a wireless system with a central station equipped with a large number of antennas surveilling a multitude of single antenna devices. The devices become active and transmit blocks of symbols sporadically. Our objective is to blindly identify the active devices and detect the transmit symbols. To this end, we exploit the sporadic nature of the device to station communication and formulate a sparse optimization problem as an integer program. Furthermore, we employ the convex relaxation of the discrete optimization variables in the problem in order reduce its computational complexity. A procedure to further lower the symbol detection errors is also discussed. Finally, the influence of system parameters on the performance of the proposed techniques is analysed using simulation results.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Joint active device identification and symbol detection using sparse constraints in massive MIMO systems\",\"authors\":\"Ganapati Hegde, M. Pesavento, M. Pfetsch\",\"doi\":\"10.23919/EUSIPCO.2017.8081298\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we consider a wireless system with a central station equipped with a large number of antennas surveilling a multitude of single antenna devices. The devices become active and transmit blocks of symbols sporadically. Our objective is to blindly identify the active devices and detect the transmit symbols. To this end, we exploit the sporadic nature of the device to station communication and formulate a sparse optimization problem as an integer program. Furthermore, we employ the convex relaxation of the discrete optimization variables in the problem in order reduce its computational complexity. A procedure to further lower the symbol detection errors is also discussed. Finally, the influence of system parameters on the performance of the proposed techniques is analysed using simulation results.\",\"PeriodicalId\":346811,\"journal\":{\"name\":\"2017 25th European Signal Processing Conference (EUSIPCO)\",\"volume\":\"40 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 25th European Signal Processing Conference (EUSIPCO)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/EUSIPCO.2017.8081298\",\"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.2017.8081298","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Joint active device identification and symbol detection using sparse constraints in massive MIMO systems
In this paper, we consider a wireless system with a central station equipped with a large number of antennas surveilling a multitude of single antenna devices. The devices become active and transmit blocks of symbols sporadically. Our objective is to blindly identify the active devices and detect the transmit symbols. To this end, we exploit the sporadic nature of the device to station communication and formulate a sparse optimization problem as an integer program. Furthermore, we employ the convex relaxation of the discrete optimization variables in the problem in order reduce its computational complexity. A procedure to further lower the symbol detection errors is also discussed. Finally, the influence of system parameters on the performance of the proposed techniques is analysed using simulation results.