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

LIU Kai; FENG Hui; YANG Tao; HU Bo

doi:10.11999/JEIT170399

Volume 40 Issue 1

Jan. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(1): 116-122

LIU Kai, FENG Hui, YANG Tao, HU Bo. Structured Sparse and Low Rank Channel Estimation in Uplink 3D-MIMO[J]. Journal of Electronics & Information Technology, 2018, 40(1): 116-122. doi: 10.11999/JEIT170399

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LIU Kai, FENG Hui, YANG Tao, HU Bo. Structured Sparse and Low Rank Channel Estimation in Uplink 3D-MIMO[J]. Journal of Electronics & Information Technology, 2018, 40(1): 116-122. doi: 10.11999/JEIT170399

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

doi: 10.11999/JEIT170399

Funds:

The National Natural Science Foundation of China (61501124)

Received Date: 2017-05-02
Rev Recd Date: 2017-09-27
Publish Date: 2018-01-19

Abstract

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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References

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