北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2016, Vol. 39 ›› Issue (5): 6-10.doi: 10.13190/j.jbupt.2016.05.002

• 论文 • 上一篇    下一篇

MIMO系统中一种基于Tucker-2模型的联合信号检测与信道估计方法

杜建和1, 田沛1, 林和昀2   

  1. 1. 中国传媒大学 信息工程学院, 北京 100024;
    2. 北京邮电大学 信息与通信工程学院, 北京 100876
  • 收稿日期:2016-04-16 出版日期:2016-10-28 发布日期:2016-12-02
  • 作者简介:杜建和(1984-),男,讲师,E-mail:dujianhe1@163.com.
  • 基金资助:
    国家高技术研究发展计划(863计划)项目(2015AA01A705);国家自然科学基金项目(61601414)

A Tucker-2 Model Based Scheme for Joint Signal Detection and Channel Estimation in MIMO Systems

DU Jian-he1, TIAN Pei1, LIN He-yun2   

  1. 1. School of Information and Engineering, Communication University of China, Beijing 100024, China;
    2. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:2016-04-16 Online:2016-10-28 Published:2016-12-02

摘要: 针对多输入多输出(MIMO)系统,提出了一种基于Tucker-2模型的联合信号检测与信道估计方法.所提方法在信源端对每个发送信息符号进行三维编码,该编码方式不仅能获得空间和时间分集,并且使得信源发送信号具备盲检测特性.在信宿端,所提方法通过对接收信号构造Tucker-2模型,并设计2种接收算法拟合该模型,从而实现了信息符号和信道的联合估计.理论分析与仿真结果表明,在未知信道状态信息的条件下,所提方法能有效地检测信号,并同时估计出信道状态信息;与传统的导频辅助信道估计方法相比,所提方法具有更好的信道估计精度.

关键词: 多输入多输出, Tucker-2模型, 分集, 盲检测, 联合估计

Abstract: A Tucker-2 model based scheme for joint signal detection and channel estimation in multiple-input multiple-output (MIMO) systems was presented. At the source node, the proposed scheme uses three-way coding for each information symbol. This coding approach can achieve space and time diversity, and it makes the transmitted signal built-in blind detection property. The received signal at the destination node is formulated as a Tucker-2 model, and then two receiver algorithms are proposed for joint signal detection and channel estimation. The theory analysis and computer simulation shows that the proposed scheme can jointly estimate the signal and the instantaneous channel state information. Compared with existing approach of pilot symbol-assisted channel estimation, the proposed scheme has higher estimation accuracy.

Key words: multiple-input multiple-output, Tucker-2 model, diversity, blind detection, jointly estimate

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