Turbo message passing based burst interference cancellation for data detection in massive MIMO-OFDM systems

Wenjun Jiang, Zhihao Ou, Xiaojun Yuan, Li Wang
{"title":"Turbo message passing based burst interference cancellation for data detection in massive MIMO-OFDM systems","authors":"Wenjun Jiang, Zhihao Ou, Xiaojun Yuan, Li Wang","doi":"10.23919/JCC.ja.2023-0164","DOIUrl":null,"url":null,"abstract":"This paper investigates the fundamental data detection problem with burst interference in massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. In particular, burst interference may occur only on data symbols but not on pilot symbols, which means that interference information cannot be premeasured. To cancel the burst interference, we first revisit the uplink multi-user system and develop a matrixform system model, where the covariance pattern and the low-rank property of the interference matrix is discussed. Then, we propose a turbo message passing based burst interference cancellation (TMP-BIC) algorithm to solve the data detection problem, where the constellation information of target data is fully exploited to refine its estimate. Furthermore, in the TMP-BIC algorithm, we design one module to cope with the interference matrix by exploiting its lowrank property. Numerical results demonstrate that the proposed algorithm can effectively mitigate the adverse effects of burst interference and approach the interference-free bound.","PeriodicalId":504777,"journal":{"name":"China Communications","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"China Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/JCC.ja.2023-0164","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

This paper investigates the fundamental data detection problem with burst interference in massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. In particular, burst interference may occur only on data symbols but not on pilot symbols, which means that interference information cannot be premeasured. To cancel the burst interference, we first revisit the uplink multi-user system and develop a matrixform system model, where the covariance pattern and the low-rank property of the interference matrix is discussed. Then, we propose a turbo message passing based burst interference cancellation (TMP-BIC) algorithm to solve the data detection problem, where the constellation information of target data is fully exploited to refine its estimate. Furthermore, in the TMP-BIC algorithm, we design one module to cope with the interference matrix by exploiting its lowrank property. Numerical results demonstrate that the proposed algorithm can effectively mitigate the adverse effects of burst interference and approach the interference-free bound.
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
基于突发干扰消除的涡轮信息传递,用于大规模 MIMO-OFDM 系统中的数据检测
本文研究了大规模多输入多输出正交频分复用(MIMO-OFDM)系统中突发干扰的基本数据检测问题。特别是,突发干扰可能只发生在数据符号上,而不发生在先导符号上,这意味着干扰信息无法预先测量。为了消除突发干扰,我们首先重新审视了上行多用户系统,并建立了一个矩阵式系统模型,其中讨论了干扰矩阵的协方差模式和低秩属性。然后,我们提出了一种基于突发干扰消除的涡轮信息传递算法(TMP-BIC)来解决数据检测问题,该算法充分利用目标数据的星座信息来完善其估计值。此外,在 TMP-BIC 算法中,我们利用干扰矩阵的低秩特性设计了一个模块来处理干扰矩阵。数值结果表明,所提出的算法能有效缓解突发干扰的不利影响,并接近无干扰边界。
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
Intellicise model transmission for semantic communication in intelligence-native 6G networks Variational learned talking-head semantic coded transmission system Physical-layer secret key generation for dual-task scenarios Intelligent dynamic heterogeneous redundancy architecture for IoT systems Joint optimization for on-demand deployment of UAVs and spectrum allocation in UAVs-assisted communication
×
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