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: 1392
- HTML全文浏览量: 193
- 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

HTML全文

参考文献(20)

NAM Y H, NG B L, SAYANA K, et al. Full-Dimension MIMO (FD-MIMO) for next generation cellular technology [J]. IEEE Communications Magazine, 2013, 51(6): 172-179. doi: 10.1109/MCOM.2013.6525612.

MARZETTA T. Noncooperative cellular wireless with unlimited numbers of base station antennas[J]. IEEE Transactions on Wireless Communications, 2010, 9(11): 3590-3600. doi: 10.1109/TWC.2010.092810.091092.

BAJWA W U, HAUPT J, SAYEED M S, et al. Compressed channel sensing: A new approach to estimating sparse multipath channels[J]. Proceedings of the IEEE, 2010, 98(6): 1058-1076. doi: 10.1109/ JPROC.2010.2042415.

TSE D and VISWANATH P. Fundamentals of Wireless Communication[M]. New York: Cambridge University Press, 2005: 290-328.

POUTANEN J, HANEDA K, SALMI J, et al. Significance of common scatters in multi-link indoor radio wave propagation [C]. Proceedings of the Fourth European Conference on Antennas and Propagation (EuCAP), Barcelona, Spain, 2010: 1-5.

XIE Hongxiang, GAO Feifei, and JIN Shi. An overview of low-rank channel estimation for massive MIMO systems[J]. IEEE Access, 2016, 4: 7313-7321. doi: 10.1109/ACCESS. 2016.2623772.

BARBOTIN Y, HORMATI A, RANGAN S, et al. Estimation of sparse MIMO channels with common support [J]. IEEE Transactions on Communications, 2012, 60(12): 3705-3716. doi: 10.1109/TCOMM.2012.091112.110439.

NAN Yang, ZHANG Li, and SUN Xin. Efficient downlink channel estimation scheme based on block-structured compressive sensing for TDD massive MU-MIMO systems[J]. IEEE Wireless Communications Letters, 2015, 4(4): 345-348. doi: 10.1109/LWC.2015.2414933.

LIU Kai, FENG Hui, YANG Tao, et al. Structured sparse channel estimation for 3D-MIMO systems[C]. IEEE Vehicular Technology Conference (VTC), Nanjing, China, 2016: 1-6.

TSAI Chengrung, CHEN Chianghen, LIU Yuhsin, et al. Joint spatially sparse channel estimation for millimeter-wave cellular systems[C]. IEEE Global Conference on Signal and Information Processing (GlobalSIP), Washington D.C., USA, 2016: 605-609.

NGUYEN S L H and GHRAYEB A. Compressive sensing-based channel estimation for massive multiuser MIMO systems[C]. IEEE Wireless Communications and Networking Conference (WCNC), Shanghai, China, 2013: 2890-2895.

FANG Jun, LI Xingjian, LI Hongbin, et al. Low-rank covariance-assisted downlink training and channel estimation for FDD massive MIMO systems[J]. IEEE Transactions on Wireless Communications, 2017, 16(3): 1935-1947. doi: 10.1109/TWC.2017.2657513.

ZHU Yi, LIU Lingjia, and ZHANG Jianzhong. Joint angle and delay estimation for 2D active broadband MIMO-OFDM systems[C]. IEEE Global Communications Conference (GLOBECOM), Atlanta, USA, 2013: 3300-3305.

RUSEK F, PERSSON D, LAU B K, et al. Scaling up MIMO: Opportunities and challenges with very large arrays[J]. IEEE Signal Processing Magazine, 2013, 30(1): 40-60. doi: 10.1109/ MSP.2011.2178495.

GAO Zhen, DAI Linglong, DAI Wei, et al. Structured compressive sensing-based spatio-temporal joint channel estimation for FDD massive MIMO[J]. IEEE Transactions on Communications, 2016, 64(2): 607-617. doi: 10.1109/ TCOMM.2015.2508809.

NEEDELL D and TROPP J A. CoSaMP: Iterative signal recovery from incomplete and inaccurate samples[J]. Communications of the ACM, 2008, 53(12): 93-100.

GOLUB G H and VAN LOAN C F. Matrix Computations [M]. Baltimore and London: Johns Hopkins University Press, 1996: 470-499.

WIPF D P and RAO B D. An empirical Bayesian strategy for solving the simultaneous sparse approximation problem[J]. IEEE Transactions on Signal Processing, 2007, 55(7): 3704-3716. doi: 10.1109/TSP.2007. 894265.

COTTER S F, RAO B D, ENGAN K, et al. Sparse solutions to linear inverse problems with multiple measurement vectors [J]. IEEE Transactions on Signal Processing, 2005, 53(7): 2477-2488. doi: 10.1109/TSP.2005.849172.

WU Xiaying, GU Lixin, WANG Wenjin, et al. Pilot design and AMP-based channel estimation for massive MIMO- OFDM uplink transmission[C]. IEEE Annual International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC), Valencia, Spain, 2016: 1-7.

施引文献

资源附件(0)

访问统计