Controllable Sparse Antenna Array for Adaptive Beamforming

We propose an <inline-formula> <tex-math notation="LaTeX">l_{0} </tex-math></inline-formula>-norm constrained normalized least-mean-square (CNLMS) adaptive beamforming algorithm for controllable sparse antenna arrays. To control the sparsity of the antenna array, an...

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Published inIEEE access Vol. 7; pp. 6412 - 6423
Main Authors Shi, Wanlu, Li, Yingsong, Zhao, Luyu, Liu, Xiaoguang
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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Adaptive algorithms
Adaptive arrays
Adaptive control
Algorithms
Antenna arrays
Antennas
Approximation algorithms
Array signal processing
Beamforming
constrained adaptive beamforming
Constraints
Convergence
Heuristic algorithms
NLMS algorithm
Signal processing algorithms
sparse controllable array
Sparsity
Stability
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ISSN2169-3536
2169-3536
DOI10.1109/ACCESS.2018.2889877

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Abstract We propose an <inline-formula> <tex-math notation="LaTeX">l_{0} </tex-math></inline-formula>-norm constrained normalized least-mean-square (CNLMS) adaptive beamforming algorithm for controllable sparse antenna arrays. To control the sparsity of the antenna array, an <inline-formula> <tex-math notation="LaTeX">l_{0} </tex-math></inline-formula>-norm penalty is used as a constraint in the CNLMS algorithm. The proposed algorithm inherits the advantages of the CNLMS algorithm in beamforming. The <inline-formula> <tex-math notation="LaTeX">l_{0} </tex-math></inline-formula>-norm constraint can force the quantities of antennas to a certain number to control the sparsity by selecting a suitable parameter. In addition, the proposed algorithm accelerates the convergence process compared with the existing algorithms in sparse array beamforming, and its convergence is presented in this paper. To reduce the computation burden, an approximating <inline-formula> <tex-math notation="LaTeX">l_{0} </tex-math></inline-formula>-norm method is employed. The performance of the proposed algorithm is analyzed through simulations for various array configurations.
AbstractList We propose an <tex-math notation="LaTeX">$l_{0}$ </tex-math>-norm constrained normalized least-mean-square (CNLMS) adaptive beamforming algorithm for controllable sparse antenna arrays. To control the sparsity of the antenna array, an <tex-math notation="LaTeX">$l_{0}$ </tex-math>-norm penalty is used as a constraint in the CNLMS algorithm. The proposed algorithm inherits the advantages of the CNLMS algorithm in beamforming. The <tex-math notation="LaTeX">$l_{0}$ </tex-math>-norm constraint can force the quantities of antennas to a certain number to control the sparsity by selecting a suitable parameter. In addition, the proposed algorithm accelerates the convergence process compared with the existing algorithms in sparse array beamforming, and its convergence is presented in this paper. To reduce the computation burden, an approximating <tex-math notation="LaTeX">$l_{0}$ </tex-math>-norm method is employed. The performance of the proposed algorithm is analyzed through simulations for various array configurations.
We propose an [Formula Omitted]-norm constrained normalized least-mean-square (CNLMS) adaptive beamforming algorithm for controllable sparse antenna arrays. To control the sparsity of the antenna array, an [Formula Omitted]-norm penalty is used as a constraint in the CNLMS algorithm. The proposed algorithm inherits the advantages of the CNLMS algorithm in beamforming. The [Formula Omitted]-norm constraint can force the quantities of antennas to a certain number to control the sparsity by selecting a suitable parameter. In addition, the proposed algorithm accelerates the convergence process compared with the existing algorithms in sparse array beamforming, and its convergence is presented in this paper. To reduce the computation burden, an approximating [Formula Omitted]-norm method is employed. The performance of the proposed algorithm is analyzed through simulations for various array configurations.
We propose an <inline-formula> <tex-math notation="LaTeX">l_{0} </tex-math></inline-formula>-norm constrained normalized least-mean-square (CNLMS) adaptive beamforming algorithm for controllable sparse antenna arrays. To control the sparsity of the antenna array, an <inline-formula> <tex-math notation="LaTeX">l_{0} </tex-math></inline-formula>-norm penalty is used as a constraint in the CNLMS algorithm. The proposed algorithm inherits the advantages of the CNLMS algorithm in beamforming. The <inline-formula> <tex-math notation="LaTeX">l_{0} </tex-math></inline-formula>-norm constraint can force the quantities of antennas to a certain number to control the sparsity by selecting a suitable parameter. In addition, the proposed algorithm accelerates the convergence process compared with the existing algorithms in sparse array beamforming, and its convergence is presented in this paper. To reduce the computation burden, an approximating <inline-formula> <tex-math notation="LaTeX">l_{0} </tex-math></inline-formula>-norm method is employed. The performance of the proposed algorithm is analyzed through simulations for various array configurations.
Author Shi, Wanlu
Li, Yingsong
Zhao, Luyu
Liu, Xiaoguang
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Snippet We propose an <inline-formula> <tex-math notation="LaTeX">l_{0} </tex-math></inline-formula>-norm constrained normalized least-mean-square (CNLMS) adaptive...
We propose an [Formula Omitted]-norm constrained normalized least-mean-square (CNLMS) adaptive beamforming algorithm for controllable sparse antenna arrays. To...
We propose an <tex-math notation="LaTeX">$l_{0}$ </tex-math>-norm constrained normalized least-mean-square (CNLMS) adaptive beamforming algorithm for...
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Adaptive algorithms
Adaptive arrays
Adaptive control
Algorithms
Antenna arrays
Antennas
Approximation algorithms
Array signal processing
Beamforming
constrained adaptive beamforming
Constraints
Convergence
Heuristic algorithms
NLMS algorithm
Signal processing algorithms
sparse controllable array
Sparsity
Stability
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Title Controllable Sparse Antenna Array for Adaptive Beamforming
URI https://ieeexplore.ieee.org/document/8598876
https://www.proquest.com/docview/2455606377
https://ieeexplore.ieee.org/ielx7/6287639/8600701/08598876.pdf
https://doaj.org/article/e5cece65c9a34672af6cb8d8c1acee89
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