Joint Nuclear Norm and &#x2113;<sub>1&#x2013;2</sub>-Regularization Sparse Channel Estimation for mmWave Massive MIMO Systems

Millimeter-wave massive MIMO can effectively improve the signal-to-noise ratio, but the high-dimensional channel matrix significantly increases the complexity of the classic channel estimation algorithm. On the other hand, millimeter-wave massive MIMO has low rank and sparsity properties in the angl...

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Main Authors: Kaiwen Yu, Min Shen, Rui Wang, Yun He
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9177126/
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spelling doaj-7e9054b686f14abeac188ce2dc374d102021-03-30T04:22:12ZengIEEEIEEE Access2169-35362020-01-01815540915541610.1109/ACCESS.2020.30192699177126Joint Nuclear Norm and &#x2113;<sub>1&#x2013;2</sub>-Regularization Sparse Channel Estimation for mmWave Massive MIMO SystemsKaiwen Yu0https://orcid.org/0000-0003-0118-3593Min Shen1https://orcid.org/0000-0002-3510-5705Rui Wang2https://orcid.org/0000-0002-2877-6834Yun He3https://orcid.org/0000-0003-4864-377XSchool of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, ChinaSchool of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, ChinaSchool of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, ChinaSchool of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, ChinaMillimeter-wave massive MIMO can effectively improve the signal-to-noise ratio, but the high-dimensional channel matrix significantly increases the complexity of the classic channel estimation algorithm. On the other hand, millimeter-wave massive MIMO has low rank and sparsity properties in the angle domain. Combining these two properties can effectively improve the channel estimation accuracy. This article proposes a novel millimeter-wave sparse channel estimation method based on joint nuclear norm and &#x2113;<sub>1-2</sub>-regularization. The basic idea of the proposed algorithm is to formulate the channel estimation problem as a compressed sensing problem. This method constructs an objective function consisted of &#x2113;<sub>1-2</sub>-regularization, and the resulting nuclear norm minimization problems is optimized via the alternating direction method of multipliers (ADMM) algorithm. The simulation results verified that the proposed method can provide better estimation accuracy compared with the state-of-the-art compressed sensing-based channel estimation methods.https://ieeexplore.ieee.org/document/9177126/ADMMlow ranksparse channel estimationℓ₁–₂-regularizationmassive MIMOmillimeter-wave
collection DOAJ
language English
format Article
sources DOAJ
author Kaiwen Yu
Min Shen
Rui Wang
Yun He
spellingShingle Kaiwen Yu
Min Shen
Rui Wang
Yun He
Joint Nuclear Norm and &#x2113;<sub>1&#x2013;2</sub>-Regularization Sparse Channel Estimation for mmWave Massive MIMO Systems
IEEE Access
ADMM
low rank
sparse channel estimation
ℓ₁–₂-regularization
massive MIMO
millimeter-wave
author_facet Kaiwen Yu
Min Shen
Rui Wang
Yun He
author_sort Kaiwen Yu
title Joint Nuclear Norm and &#x2113;<sub>1&#x2013;2</sub>-Regularization Sparse Channel Estimation for mmWave Massive MIMO Systems
title_short Joint Nuclear Norm and &#x2113;<sub>1&#x2013;2</sub>-Regularization Sparse Channel Estimation for mmWave Massive MIMO Systems
title_full Joint Nuclear Norm and &#x2113;<sub>1&#x2013;2</sub>-Regularization Sparse Channel Estimation for mmWave Massive MIMO Systems
title_fullStr Joint Nuclear Norm and &#x2113;<sub>1&#x2013;2</sub>-Regularization Sparse Channel Estimation for mmWave Massive MIMO Systems
title_full_unstemmed Joint Nuclear Norm and &#x2113;<sub>1&#x2013;2</sub>-Regularization Sparse Channel Estimation for mmWave Massive MIMO Systems
title_sort joint nuclear norm and &#x2113;<sub>1&#x2013;2</sub>-regularization sparse channel estimation for mmwave massive mimo systems
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2020-01-01
description Millimeter-wave massive MIMO can effectively improve the signal-to-noise ratio, but the high-dimensional channel matrix significantly increases the complexity of the classic channel estimation algorithm. On the other hand, millimeter-wave massive MIMO has low rank and sparsity properties in the angle domain. Combining these two properties can effectively improve the channel estimation accuracy. This article proposes a novel millimeter-wave sparse channel estimation method based on joint nuclear norm and &#x2113;<sub>1-2</sub>-regularization. The basic idea of the proposed algorithm is to formulate the channel estimation problem as a compressed sensing problem. This method constructs an objective function consisted of &#x2113;<sub>1-2</sub>-regularization, and the resulting nuclear norm minimization problems is optimized via the alternating direction method of multipliers (ADMM) algorithm. The simulation results verified that the proposed method can provide better estimation accuracy compared with the state-of-the-art compressed sensing-based channel estimation methods.
topic ADMM
low rank
sparse channel estimation
ℓ₁–₂-regularization
massive MIMO
millimeter-wave
url https://ieeexplore.ieee.org/document/9177126/
work_keys_str_mv AT kaiwenyu jointnuclearnormandx2113sub1x20132subregularizationsparsechannelestimationformmwavemassivemimosystems
AT minshen jointnuclearnormandx2113sub1x20132subregularizationsparsechannelestimationformmwavemassivemimosystems
AT ruiwang jointnuclearnormandx2113sub1x20132subregularizationsparsechannelestimationformmwavemassivemimosystems
AT yunhe jointnuclearnormandx2113sub1x20132subregularizationsparsechannelestimationformmwavemassivemimosystems
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