CLC number: TN911.5
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2017-12-20
Cited: 0
Clicked: 9074
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",
volume="18",
number="12",
pages="2082-2100",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1601635"
}
%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
TY - JOUR
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
Abstract: 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.
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