{"title":"基于天线功率约束的MMSE预编解码器设计","authors":"I. Lu, Jialing Li, E. Lu","doi":"10.1109/ICSPCS.2009.5306440","DOIUrl":null,"url":null,"abstract":"Two complementary numerical approaches, the generalized iterative approach (GIA) and the transmit covariance optimization approach (TCOA) are proposed for jointly designing the minimum mean square error (MMSE) precoders and decoders in uplink multiuser multiple-input-multiple-output (MIMO) systems with a per-antenna power constraint. The TCOA always give optimum solution but works only when the source covariance matrices are projection matrices multiplied by the same constant and the rank constraint on the precoders is relaxed. On the other hand, the GIA does not have these restrictions. Furthermore, it is able to deal with arbitrary source covariances and allows arbitrary numbers of data streams. However, under these more general conditions, the GIA results are only guaranteed to be locally optimum. Regarding computational efficiency, the TCOA is more efficient at high transmission power and the GIA is more efficient at low transmission power when both approaches are applicable. Furthermore, the GIA and the TCOA are equivalent and both are optimum if the transmit covariance matrices obtained from the MMSE design are full rank. Numerical results show that the MSE and BER performances of the two approaches with the more practical per-antenna power constraint are very similar to those with the less practical per-user power constraint.","PeriodicalId":356711,"journal":{"name":"2009 3rd International Conference on Signal Processing and Communication Systems","volume":"69 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Novel MMSE precoder and decoder designs subject to per-antenna power constraint for uplink multiuser MIMO systems\",\"authors\":\"I. Lu, Jialing Li, E. Lu\",\"doi\":\"10.1109/ICSPCS.2009.5306440\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Two complementary numerical approaches, the generalized iterative approach (GIA) and the transmit covariance optimization approach (TCOA) are proposed for jointly designing the minimum mean square error (MMSE) precoders and decoders in uplink multiuser multiple-input-multiple-output (MIMO) systems with a per-antenna power constraint. The TCOA always give optimum solution but works only when the source covariance matrices are projection matrices multiplied by the same constant and the rank constraint on the precoders is relaxed. On the other hand, the GIA does not have these restrictions. Furthermore, it is able to deal with arbitrary source covariances and allows arbitrary numbers of data streams. However, under these more general conditions, the GIA results are only guaranteed to be locally optimum. Regarding computational efficiency, the TCOA is more efficient at high transmission power and the GIA is more efficient at low transmission power when both approaches are applicable. Furthermore, the GIA and the TCOA are equivalent and both are optimum if the transmit covariance matrices obtained from the MMSE design are full rank. Numerical results show that the MSE and BER performances of the two approaches with the more practical per-antenna power constraint are very similar to those with the less practical per-user power constraint.\",\"PeriodicalId\":356711,\"journal\":{\"name\":\"2009 3rd International Conference on Signal Processing and Communication Systems\",\"volume\":\"69 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 3rd International Conference on Signal Processing and Communication Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSPCS.2009.5306440\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 3rd International Conference on Signal Processing and Communication Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSPCS.2009.5306440","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Novel MMSE precoder and decoder designs subject to per-antenna power constraint for uplink multiuser MIMO systems
Two complementary numerical approaches, the generalized iterative approach (GIA) and the transmit covariance optimization approach (TCOA) are proposed for jointly designing the minimum mean square error (MMSE) precoders and decoders in uplink multiuser multiple-input-multiple-output (MIMO) systems with a per-antenna power constraint. The TCOA always give optimum solution but works only when the source covariance matrices are projection matrices multiplied by the same constant and the rank constraint on the precoders is relaxed. On the other hand, the GIA does not have these restrictions. Furthermore, it is able to deal with arbitrary source covariances and allows arbitrary numbers of data streams. However, under these more general conditions, the GIA results are only guaranteed to be locally optimum. Regarding computational efficiency, the TCOA is more efficient at high transmission power and the GIA is more efficient at low transmission power when both approaches are applicable. Furthermore, the GIA and the TCOA are equivalent and both are optimum if the transmit covariance matrices obtained from the MMSE design are full rank. Numerical results show that the MSE and BER performances of the two approaches with the more practical per-antenna power constraint are very similar to those with the less practical per-user power constraint.