A LARGE-SCALE EXTENSION OF SPARSE-CODE MULTIPLE-ACCESS SYSTEM

Chao Yang, Shusen Jing, X. Liang, Zaichen Zhang, X. You, Chuan Zhang
{"title":"A LARGE-SCALE EXTENSION OF SPARSE-CODE MULTIPLE-ACCESS SYSTEM","authors":"Chao Yang, Shusen Jing, X. Liang, Zaichen Zhang, X. You, Chuan Zhang","doi":"10.1109/GlobalSIP.2018.8646492","DOIUrl":null,"url":null,"abstract":"Sparse-code multiple-access (SCMA) is an effective non-orthogonal multiple-access (NOMA) technique, which ranks one of the most promising candidates for future wireless, because of its outstanding performance. However, most of the existing work prefers low-connectivity SCMA systems, which actually cannot fulfill their priorities for massive connection. The difficulties in designing suitable factor graph matrix are responsible for this situation. In this paper, we propose a design manner of factor graph matrix to realize SCMA with large-scale customers. Quasi-cyclic property with shifting is introduced in SCMA factor graph matrix, based on the rules and restrictions of SCMA design including the column weight and the overloading factor. For numerical analysis, we introduce a performance function to show the effectiveness of our proposed SCMA system, and the results reveal that our work are better than conventional ones.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"131 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GlobalSIP.2018.8646492","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

Sparse-code multiple-access (SCMA) is an effective non-orthogonal multiple-access (NOMA) technique, which ranks one of the most promising candidates for future wireless, because of its outstanding performance. However, most of the existing work prefers low-connectivity SCMA systems, which actually cannot fulfill their priorities for massive connection. The difficulties in designing suitable factor graph matrix are responsible for this situation. In this paper, we propose a design manner of factor graph matrix to realize SCMA with large-scale customers. Quasi-cyclic property with shifting is introduced in SCMA factor graph matrix, based on the rules and restrictions of SCMA design including the column weight and the overloading factor. For numerical analysis, we introduce a performance function to show the effectiveness of our proposed SCMA system, and the results reveal that our work are better than conventional ones.
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
稀疏码多址系统的大规模扩展
稀疏码多址(SCMA)是一种有效的非正交多址(NOMA)技术,以其优异的性能成为未来无线通信最有前途的技术之一。然而,现有的大多数工作都倾向于低连接的SCMA系统,这实际上无法满足其对大连接的优先级要求。设计合适的因子图矩阵的困难是造成这种情况的原因。本文提出了一种因子图矩阵的设计方法来实现大规模客户的供应链管理。在考虑柱重和超载系数等因素的基础上,引入了SCMA因子图矩阵具有位移的拟循环性质。在数值分析中,我们引入了一个性能函数来证明我们所提出的SCMA系统的有效性,结果表明我们的工作比传统的系统要好。
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
ADAPTIVE CSP FOR USER INDEPENDENCE IN MI-BCI PARADIGM FOR UPPER LIMB STROKE REHABILITATION SPATIAL FOURIER TRANSFORM FOR DETECTION AND ANALYSIS OF PERIODIC ASTROPHYSICAL PULSES CNN ARCHITECTURES FOR GRAPH DATA OVERT SPEECH RETRIEVAL FROM NEUROMAGNETIC SIGNALS USING WAVELETS AND ARTIFICIAL NEURAL NETWORKS CNN BASED RICIAN K FACTOR ESTIMATION FOR NON-STATIONARY INDUSTRIAL FADING CHANNEL
×
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