Full Text:   <664>

Summary:  <171>

CLC number: TN911.5

On-line Access: 2018-02-06

Received: 2016-10-14

Revision Accepted: 2017-05-22

Crosschecked: 2017-12-20

Cited: 0

Clicked: 2365

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.12 P.2082-2100


Compressed sensing-based structured joint channel estimation in a multi-user massive MIMO system

Author(s):  Ruo-yu Zhang, Hong-lin Zhao, Shao-bo Jia

Affiliation(s):  Communication Research Center, Harbin Institute of Technology, Harbin 150080, China

Corresponding email(s):   hlzhao@hit.edu.cn

Key Words:  Compressed sensing, Multi-user massive multiple input multiple output (MIMO), Frequency-division duplexing, Structured joint channel estimation, Pilot overhead reduction

Ruo-yu Zhang, Hong-lin Zhao, Shao-bo Jia. Compressed sensing-based structured joint channel estimation in a multi-user massive MIMO system[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(12): 2082-2100.

@article{title="Compressed sensing-based structured joint channel estimation in a multi-user massive MIMO system",
author="Ruo-yu Zhang, Hong-lin Zhao, Shao-bo Jia",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Compressed sensing-based structured joint channel estimation in a multi-user massive MIMO system
%A Ruo-yu Zhang
%A Hong-lin Zhao
%A Shao-bo Jia
%J Frontiers of Information Technology & Electronic Engineering
%V 18
%N 12
%P 2082-2100
%@ 2095-9184
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601635

T1 - Compressed sensing-based structured joint channel estimation in a multi-user massive MIMO system
A1 - Ruo-yu Zhang
A1 - Hong-lin Zhao
A1 - Shao-bo Jia
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
IS - 12
SP - 2082
EP - 2100
%@ 2095-9184
Y1 - 2017
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1601635

Acquisition of accurate channel state information (CSI) at transmitters results in a huge pilot overhead in massive multiple input multiple output (MIMO) systems due to the large number of antennas in the base station (BS). To reduce the overwhelming pilot overhead in such systems, a structured joint channel estimation scheme employing compressed sensing (CS) theory is proposed. Specifically, the channel sparsity in the angular domain due to the practical scattering environment is analyzed, where common sparsity and individual sparsity structures among geographically neighboring users exist in multi-user massive MIMO systems. Then, by equipping each user with multiple antennas, the pilot overhead can be alleviated in the framework of CS and the channel estimation quality can be improved. Moreover, a structured joint matching pursuit (SJMP) algorithm at the BS is proposed to jointly estimate the channel of users with reduced pilot overhead. Furthermore, the probability upper bound of common support recovery and the upper bound of channel estimation quality using the proposed SJMP algorithm are derived. Simulation results demonstrate that the proposed SJMP algorithm can achieve a higher system performance than those of existing algorithms in terms of pilot overhead and achievable rate.


概要:由于在基站处部署了大量天线,因而在大规模MIMO(multiple input multiple output)系统中发射机端获取信道状态信息需要大量的导频开销。为降低该系统所消耗的导频资源,本文基于压缩感知技术,提出了一种结构化联合信道估计的方法。首先分析了实际散射环境造成的角度域信道稀疏性,其中大规模MIMO系统中地理位置临近用户的信道矩阵存在共有稀疏结构和独立稀疏结构。同时,在压缩感知的框架下,用户配备多根天线能够进一步缓解导频开销的问题并能够提高信道估计的质量。在此基础上,本文提出了一种结构化联合信道估计算法,该算法能够以低导频开销联合估计多个用户的信道状态信息。此外,提供了该算法的共有支撑集恢复的概率上界和信道估计质量的上界。仿真结果表明该结构化联合信道估计算法能提供比现有算法更低的导频开销和更高的系统吞吐量。


Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Barbotin, Y., Hormati, A., Rangan, S., et al., 2012. Estimation of sparse MIMO channels with common support. IEEE Trans. Commun., 60(12):3705-3716.

[2]Baum, D.S., Hansen, J., Salo, J., 2005. An interim channel model for beyond-3G systems: extending the 3GPP spatial channel model (SCM). IEEE 61st Vehicular Technology Conf., p.3132-3136.

[3]Berger, C.R., Wang, Z.H., Huang, J.Z., et al., 2010. Application of compressive sensing to sparse channel estimation. IEEE Commun. Mag., 48(11):164-174.

[4]Björnson, E., Larsson, E.G., Marzetta, T.L., 2015. Massive MIMO: ten myths and one critical question. IEEE Commun. Mag., 54(2):114-123.

[5]Bogale, T.E., Vandendorpe, L., Chalise, B.K., 2012. Robust transceiver optimization for downlink coordinated base station systems: distributed algorithm. IEEE Trans. Signal Process., 60(1):337-350.

[6]Chen, Y., Qin, Z., 2015. Gradient-based compressive image fusion. Front. Inform. Technol. Electron. Eng., 16(3):227-237.

[7]Choi, J., Love, D.J., Bidigare, P., 2014. Downlink training techniques for FDD massive MIMO systems: open-loop and closed-loop training with memory. IEEE J. Sel. Top. Signal Process., 8(5):802-814.

[8]Dai, L.L., Wang, J.T., Wang, Z.C., et al., 2013. Spectrum-and energy-efficient OFDM based on simultaneous multi-channel reconstruction. IEEE Trans. Signal Process., 61(23):6047-6059.

[9]Dai, W., Milenkovic, O., 2009. Subspace pursuit for compressive sensing signal reconstruction. IEEE Trans. Inform. Theory, 55(5):2230-2249.

[10]Dasgupta, S., Gupta, A., 2003. An elementary proof of a theorem of Johnson and Lindenstrauss. Rand. Struct. Algor., 22(1):60-65.

[11]Donoho, D.L., 2006. Compressed sensing. IEEE Trans. Inform. Theory, 52(4):1289-1306.

[12]Eldar, Y.C., Kuppinger, P., Bölcskei, H., 2010. Block-sparse signals: uncertainty relations and efficient recovery. IEEE Trans. Signal Process., 58(6):3042-3054.

[13]Gao, X., Edfors, O., Rusek, F., et al., 2011. Linear pre-coding performance in measured very-large MIMO channels. IEEE Vehicular Technology Conf., p.1-5.

[14]Gao, Z., Dai, L.L., Wang, Z., 2014. Structured compressive sensing based superimposed pilot design in downlink large-scale MIMO systems. Electron. Lett., 50(12):896-898.

[15]Gao, Z., Dai, L.L., Wang, Z., et al., 2015. Spatially common sparsity based adaptive channel estimation and feedback for FDD massive MIMO. IEEE Trans. Signal Process., 63(23):6169-6183.

[16]Gao, Z., Dai, L.L., Dai, W., et al., 2016. Structured compressive sensing-based spatio-temporal joint channel estimation for FDD massive MIMO. IEEE Trans. Commun., 64(2):601-617.

[17]Hoydis, J., Hoek, C., Wild, T., et al., 2012. Channel measurements for large antenna arrays. IEEE Int. Symp. on Wireless Communication Systems, p.811-815.

[18]Hoydis, J., Ten Brink, S., Debbah, M., 2013. Massive MIMO in the UL/DL of cellular networks: how many antennas do we need IEEE J. Sel. Areas Commun., 31(2):160-171.

[19]Hu, D., Wang, X.D., He, L.H., 2013. A new sparse channel estimation and tracking method for time-varying OFDM systems. IEEE Trans. Veh. Technol., 62(9):4648-4653.

[20]Ketonen, J., Juntti, M., Cavallaro, J.R., 2010. Performance-complexity comparison of receivers for a LTE MIMO-OFDM system. IEEE Trans. Signal Process., 58(6): 3360-3372.

[21]Lee, B., Choi, J., Seol, J.Y., et al., 2015. Antenna grouping based feedback compression for FDD-based massive MIMO systems. IEEE Trans. Commun., 63(9):3261-3274.

[22]Lu, L., Li, G.Y., Swindlehurst, A.L., et al., 2014. An overview of massive MIMO: benefits and challenges. IEEE J. Sel. Top. Signal Process., 8(5):742-758.

[23]Noh, S., Zoltowski, M.D., Sung, Y., et al., 2014. Pilot beam pattern design for channel estimation in massive MIMO systems. IEEE J. Sel. Top. Signal Process., 8(5):787-801.

[24]Qi, C.H., Wu, L.N., 2014. Uplink channel estimation for massive MIMO systems exploring joint channel sparsity. Electron. Lett., 50(23):1770-1772.

[25]Rao, X.B., Lau, V.K.N., 2014. Distributed compressive CSIT estimation and feedback for FDD multi-user massive MIMO systems. IEEE Trans. Signal Process., 62(12): 3261-3271.

[26]Tropp, J.A., Gilbert, A.C., 2007. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inform. Theory, 53(12):4655-4666.

[27]Tropp, J.A., Gilbert, A.C., Strauss, M.J., 2006. Algorithms for simultaneous sparse approximation. Part I: greedy pursuit. Signal Process., 86(3):572-588.

[28]Tse, D., Viswanath, P., 2005. Fundamentals of Wireless Communication. Cambridge University Press, New York, p.309-330.

[29]Yin, H.F., Gesbert, D., Filippou, M., et al., 2012. A coordinated approach to channel estimation in large-scale multiple-antenna systems. IEEE J. Sel. Areas Commun., 31(2): 264-273.

[30]Zhang, Z.Y., Teh, K.C., Li, K.H., 2014. Application of compressive sensing to limited feedback strategy in large-scale multiple-input single-output cellular networks. IET Commun., 8(6):947-955.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE