{"title":"MIMO系统中考虑信道状态的多用户分组建模与仿真","authors":"Prasad Rayi, M. Prasad, P. Kishore","doi":"10.1109/SPACES.2015.7058254","DOIUrl":null,"url":null,"abstract":"In this paper we presented limited feedback schemes for Multi user-Multiple Input and Multiple Output (MU-MIMO) transmit antenna downlink systems. We investigate performance analysis of the feedback strategies based on the knowledge of channel state information (CSI) at base station (BS). it can be most desirable to generate high sum capacity, thus there is a constraint on total feedback load and per user feedback bits. It can be extensively evaluated the ZFRVQ, RBF and PU2RC feedback algorithms for sum rate analysis. The channel model considers as AWGN and Rayleigh fading model. The combination of multi-user feedback methods and multiple antennas' can enhance the sum capacity of MU-MIMO systems. In order to achieve high capacity thus, the system requires perfect CSI. Our system model presented to obtain high-quality feedback with the strong priority and also achieving larger sum rate with limited number of antenna's from each one of the receivers. We produced larger capacity more effectively and efficiently, instead of capturing a few feedback bits from more users. This implies that the system design, correlates very strongly for achieving larger sum capacity with CSI from MS's or users, Hence there is a tradeoff between total feedback load to the sum rate of the system. The Simulation results presented here are subjected to Monte Carlo simulation with MATLAB 2013a.","PeriodicalId":432479,"journal":{"name":"2015 International Conference on Signal Processing and Communication Engineering Systems","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Modelling, simulation of multi-user grouping considering channel states in MIMO systems\",\"authors\":\"Prasad Rayi, M. Prasad, P. Kishore\",\"doi\":\"10.1109/SPACES.2015.7058254\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we presented limited feedback schemes for Multi user-Multiple Input and Multiple Output (MU-MIMO) transmit antenna downlink systems. We investigate performance analysis of the feedback strategies based on the knowledge of channel state information (CSI) at base station (BS). it can be most desirable to generate high sum capacity, thus there is a constraint on total feedback load and per user feedback bits. It can be extensively evaluated the ZFRVQ, RBF and PU2RC feedback algorithms for sum rate analysis. The channel model considers as AWGN and Rayleigh fading model. The combination of multi-user feedback methods and multiple antennas' can enhance the sum capacity of MU-MIMO systems. In order to achieve high capacity thus, the system requires perfect CSI. Our system model presented to obtain high-quality feedback with the strong priority and also achieving larger sum rate with limited number of antenna's from each one of the receivers. We produced larger capacity more effectively and efficiently, instead of capturing a few feedback bits from more users. This implies that the system design, correlates very strongly for achieving larger sum capacity with CSI from MS's or users, Hence there is a tradeoff between total feedback load to the sum rate of the system. The Simulation results presented here are subjected to Monte Carlo simulation with MATLAB 2013a.\",\"PeriodicalId\":432479,\"journal\":{\"name\":\"2015 International Conference on Signal Processing and Communication Engineering Systems\",\"volume\":\"39 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-03-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 International Conference on Signal Processing and Communication Engineering Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SPACES.2015.7058254\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 International Conference on Signal Processing and Communication Engineering Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SPACES.2015.7058254","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Modelling, simulation of multi-user grouping considering channel states in MIMO systems
In this paper we presented limited feedback schemes for Multi user-Multiple Input and Multiple Output (MU-MIMO) transmit antenna downlink systems. We investigate performance analysis of the feedback strategies based on the knowledge of channel state information (CSI) at base station (BS). it can be most desirable to generate high sum capacity, thus there is a constraint on total feedback load and per user feedback bits. It can be extensively evaluated the ZFRVQ, RBF and PU2RC feedback algorithms for sum rate analysis. The channel model considers as AWGN and Rayleigh fading model. The combination of multi-user feedback methods and multiple antennas' can enhance the sum capacity of MU-MIMO systems. In order to achieve high capacity thus, the system requires perfect CSI. Our system model presented to obtain high-quality feedback with the strong priority and also achieving larger sum rate with limited number of antenna's from each one of the receivers. We produced larger capacity more effectively and efficiently, instead of capturing a few feedback bits from more users. This implies that the system design, correlates very strongly for achieving larger sum capacity with CSI from MS's or users, Hence there is a tradeoff between total feedback load to the sum rate of the system. The Simulation results presented here are subjected to Monte Carlo simulation with MATLAB 2013a.