{"title":"Comparison of antenna array algorithms for CDMA mobile communications systems","authors":"R. Carrasco","doi":"10.1109/ITS.1998.713145","DOIUrl":null,"url":null,"abstract":"Summary form only given. Various linear and nonlinear algorithms for adaptive antenna arrays in code division multiple access (CDMA) mobile communication systems are investigated. The computer-simulated system model incorporates QPSK-CDMA modulation, demodulation, diversity channels and adaptive array structures. Diversity channels are time-varying and have frequency-selective fading characteristics. Simulation results show that the optimal technique in terms of performance and computational complexity is based on a recurrent neural network (RNN) structure.","PeriodicalId":205350,"journal":{"name":"ITS'98 Proceedings. SBT/IEEE International Telecommunications Symposium (Cat. No.98EX202)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1998-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ITS'98 Proceedings. SBT/IEEE International Telecommunications Symposium (Cat. No.98EX202)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITS.1998.713145","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Summary form only given. Various linear and nonlinear algorithms for adaptive antenna arrays in code division multiple access (CDMA) mobile communication systems are investigated. The computer-simulated system model incorporates QPSK-CDMA modulation, demodulation, diversity channels and adaptive array structures. Diversity channels are time-varying and have frequency-selective fading characteristics. Simulation results show that the optimal technique in terms of performance and computational complexity is based on a recurrent neural network (RNN) structure.