A multiuser detection method based on support vector machine

Tao Yang, Jianying Xie
{"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}
引用次数: 1

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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于支持向量机的多用户检测方法
本文提出了一种基于支持向量机(SVM)的多用户检测器,该检测器将接收向量分为+1和-1两类来实现检测。与MMSE检测器不同的是,SVM方法可以找到一个最优的超平面来分离训练数据中的+1和-1。仿真结果表明,在瑞利信道下,与最小均方误差(MMSE)检测器相比,该检测器具有较低的误码率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Plenary Talk: Digital-Twin Fluid Engineering APPLYING MACHINE LEARNING TECHNIQUES IN DETECTING BACTERIAL VAGINOSIS. OPTICAL COHERENCE TOMOGRAPHY HEART TUBE IMAGE DENOISING BASED ON CONTOURLET TRANSFORM. The multistage support vector machine Anti-control of chaos based on fuzzy neural networks inverse system method
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1