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CLC number: TN911.5

On-line Access: 2018-02-06

Received: 2016-10-14

Revision Accepted: 2017-05-22

Crosschecked: 2017-12-20

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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.

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%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
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T1 - Compressed sensing-based structured joint channel estimation in a multi-user massive MIMO system
A1 - Ruo-yu Zhang
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A1 - Shao-bo Jia
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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


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