{"title":"基于支持向量机的多用户检测方法","authors":"Tao Yang, Jianying Xie","doi":"10.1109/ICMLC.2002.1176777","DOIUrl":null,"url":null,"abstract":"In this paper, a multi-user detector based on a Support Vector Machine (SVM) is proposed, which divides the receiving vector into two classes, +1 and -1, to attain detection. Differing from the MMSE detector, the SVM method can find an optimal hyperplane to separate the +1 and -1 from the training data. Simulation results show that under the Rayleigh channel, this detector can achieve a relatively low BER in comparison with the minimum mean square error (MMSE) detector.","PeriodicalId":90702,"journal":{"name":"Proceedings. International Conference on Machine Learning and Cybernetics","volume":"27 1","pages":"373-375 vol.1"},"PeriodicalIF":0.0000,"publicationDate":"2002-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A multiuser detection method based on support vector machine\",\"authors\":\"Tao Yang, Jianying Xie\",\"doi\":\"10.1109/ICMLC.2002.1176777\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a multi-user detector based on a Support Vector Machine (SVM) is proposed, which divides the receiving vector into two classes, +1 and -1, to attain detection. Differing from the MMSE detector, the SVM method can find an optimal hyperplane to separate the +1 and -1 from the training data. Simulation results show that under the Rayleigh channel, this detector can achieve a relatively low BER in comparison with the minimum mean square error (MMSE) detector.\",\"PeriodicalId\":90702,\"journal\":{\"name\":\"Proceedings. International Conference on Machine Learning and Cybernetics\",\"volume\":\"27 1\",\"pages\":\"373-375 vol.1\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-11-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. International Conference on Machine Learning and Cybernetics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMLC.2002.1176777\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. International Conference on Machine Learning and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLC.2002.1176777","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A multiuser detection method based on support vector machine
In this paper, a multi-user detector based on a Support Vector Machine (SVM) is proposed, which divides the receiving vector into two classes, +1 and -1, to attain detection. Differing from the MMSE detector, the SVM method can find an optimal hyperplane to separate the +1 and -1 from the training data. Simulation results show that under the Rayleigh channel, this detector can achieve a relatively low BER in comparison with the minimum mean square error (MMSE) detector.
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