上行3D-MIMO中利用结构稀疏低秩特性的信道估计算法

刘凯; 冯辉; 杨涛; 胡波

doi:10.11999/JEIT170399

上行3D-MIMO中利用结构稀疏低秩特性的信道估计算法

doi: 10.11999/JEIT170399

刘凯¹,
冯辉¹,
杨涛¹,
胡波¹

基金项目:

国家自然科学基金(61501124)

计量
- 文章访问数: 1450
- HTML全文浏览量: 203
- PDF下载量: 185
- 被引次数: 0
出版历程
- 收稿日期: 2017-05-02
- 修回日期: 2017-09-27
- 刊出日期: 2018-01-19

Structured Sparse and Low Rank Channel Estimation in Uplink 3D-MIMO

Funds:

The National Natural Science Foundation of China (61501124)

摘要

摘要: 3维多输入多输出(3D-MIMO)系统能有效提升频谱效率，提高系统容量。但用户数和天线数的剧增，无法保证所有用户的导频都正交，给3D-MIMO信道估计带来估计精度下降和复杂度增加等问题。该文分析了上行3D-MIMO系统信道的结构稀疏特性和低秩特性，并基于这些特性提出一种信道估计算法，给出了算法的收敛性和复杂度。仿真结果表明估计算法能准确地恢复3D-MIMO的信道系数，并有较低的复杂度。
- 信道估计 /
- 3D-MIMO /
- 结构稀疏 /
- 低秩 /
- 匹配追踪
Abstract: Three Dimension Multi-Input Multi-Output (3D-MIMO) systems can effectively improve frequency efficiency and system capacity. However, with the growing number of antennas and users, pilot sequences are non- orthogonal, which will affect the accuracy of 3D-MIMO channel estimation and increase complexity. In this paper, the structured sparseness and low rank property of 3D-MIMO channel are studied. By taking advantage of these properties, a channel estimation algorithm is proposed, and the convergence and complexity of the algorithm are analyzed. Simulation results verify that the proposed algorithm can accurately recover 3D-MIMO channel with low complexity.
- Channel estimation /
- 3D-MIMO /
- Structured sparseness /
- Low rank /
- Matching pursuit

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参考文献(20)

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