{"title":"基于压缩感知的LS和MMSE信道估计算法性能分析","authors":"A. Munshi, S. Unnikrishnan","doi":"10.24138/JCOMSS.V17I1.1084","DOIUrl":null,"url":null,"abstract":"In this paper, the optimality of Compressive Sensing based Least Square (LS-CS) and Compressive Sensing based Minimum Mean Square (MMSE-CS) channel estimation algorithms in Multi Input Multi Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) system is investigated for a sparse communication channel. The performance of LS, MMSE, LS-CS and MMSE-CS channel estimation algorithms in terms of sparsity of the channel, compressive sensing and mathematical complexity is investigated and analyzed so that optimum ranges can be recommended.","PeriodicalId":38910,"journal":{"name":"Journal of Communications Software and Systems","volume":"17 1","pages":"13-19"},"PeriodicalIF":0.6000,"publicationDate":"2021-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Performance Analysis of Compressive Sensing based LS and MMSE Channel Estimation Algorithm\",\"authors\":\"A. Munshi, S. Unnikrishnan\",\"doi\":\"10.24138/JCOMSS.V17I1.1084\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, the optimality of Compressive Sensing based Least Square (LS-CS) and Compressive Sensing based Minimum Mean Square (MMSE-CS) channel estimation algorithms in Multi Input Multi Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) system is investigated for a sparse communication channel. The performance of LS, MMSE, LS-CS and MMSE-CS channel estimation algorithms in terms of sparsity of the channel, compressive sensing and mathematical complexity is investigated and analyzed so that optimum ranges can be recommended.\",\"PeriodicalId\":38910,\"journal\":{\"name\":\"Journal of Communications Software and Systems\",\"volume\":\"17 1\",\"pages\":\"13-19\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2021-02-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Communications Software and Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.24138/JCOMSS.V17I1.1084\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Communications Software and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.24138/JCOMSS.V17I1.1084","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Performance Analysis of Compressive Sensing based LS and MMSE Channel Estimation Algorithm
In this paper, the optimality of Compressive Sensing based Least Square (LS-CS) and Compressive Sensing based Minimum Mean Square (MMSE-CS) channel estimation algorithms in Multi Input Multi Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) system is investigated for a sparse communication channel. The performance of LS, MMSE, LS-CS and MMSE-CS channel estimation algorithms in terms of sparsity of the channel, compressive sensing and mathematical complexity is investigated and analyzed so that optimum ranges can be recommended.
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