Channel Estimation using Block Sparse Joint Orthogonal Matching Pursuit in Massive MIMO Systems

Nasser Sadeghi, M. Azghani
{"title":"Channel Estimation using Block Sparse Joint Orthogonal Matching Pursuit in Massive MIMO Systems","authors":"Nasser Sadeghi, M. Azghani","doi":"10.1109/CSICC52343.2021.9420624","DOIUrl":null,"url":null,"abstract":"The channel estimation of the muti-user massive MIMO systems is a crucial task which enables us to leverage their high degrees of freedom. Due to the large number of base station antennas and consequently the huge number of channel paths, the massive MIMO channel estimation becomes more challenging. In this paper, we suggest a sparsity-based algorithm to estimate the channels more efficiently. To this end, we would offer a problem modelling to exploit the spatial correlation among different antennas of the BS as well as the inter-user similarity of the channel supports. An iterative thresholding technique has been suggested to approximate the channel matrix. The simulation results confirm that the proposed method has outstanding performance compared to its counterparts.","PeriodicalId":374593,"journal":{"name":"2021 26th International Computer Conference, Computer Society of Iran (CSICC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 26th International Computer Conference, Computer Society of Iran (CSICC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSICC52343.2021.9420624","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

The channel estimation of the muti-user massive MIMO systems is a crucial task which enables us to leverage their high degrees of freedom. Due to the large number of base station antennas and consequently the huge number of channel paths, the massive MIMO channel estimation becomes more challenging. In this paper, we suggest a sparsity-based algorithm to estimate the channels more efficiently. To this end, we would offer a problem modelling to exploit the spatial correlation among different antennas of the BS as well as the inter-user similarity of the channel supports. An iterative thresholding technique has been suggested to approximate the channel matrix. The simulation results confirm that the proposed method has outstanding performance compared to its counterparts.
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
大规模MIMO系统中基于块稀疏联合正交匹配跟踪的信道估计
多用户大规模MIMO系统的信道估计是一项至关重要的任务,它使我们能够充分利用其高度的自由度。由于基站天线数量庞大,信道路径数量庞大,使得大规模MIMO信道估计变得更加具有挑战性。在本文中,我们提出了一种基于稀疏性的算法来更有效地估计信道。为此,我们将提供一个问题建模,以利用BS不同天线之间的空间相关性以及信道支持的用户间相似性。提出了一种迭代阈值技术来近似信道矩阵。仿真结果表明,该方法具有较好的性能。
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
Transfer Learning for End-to-End ASR to Deal with Low-Resource Problem in Persian Language An SDN-based Firewall for Networks with Varying Security Requirements A Face-Mask Detection Approach based on YOLO Applied for a New Collected Dataset Telegram group recommendation based on users' migration Design of an IoT-based Flood Early Detection System using Machine Learning
×
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