A Kronecker product CLMS algorithm for adaptive beamforming

In this paper, an adaptive algorithm is derived by considering that the beamforming vector can be decomposed as a Kronecker product of two smaller vectors. Such a decomposition leads to a joint optimization problem, which is then solved by using an alternating optimization strategy along with the st...

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Bibliographic Details
Published inDigital signal processing Vol. 111; p. 102968
Main Authors Kuhn, Eduardo Vinicius, Pitz, Ciro André, Matsuo, Marcos Vinicius, Bakri, Khaled Jamal, Seara, Rui, Benesty, Jacob
Format Journal Article
LanguageEnglish
Published Elsevier Inc 01.04.2021
Subjects
Adaptive beamforming
Antenna arrays
CLMS algorithm
Kronecker product
Mobile communications
Kronecker product
CLMS algorithm
Mobile communications
Adaptive beamforming
Antenna arrays
Online AccessGet full text
ISSN1051-2004
1095-4333
DOI10.1016/j.dsp.2021.102968

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Summary:In this paper, an adaptive algorithm is derived by considering that the beamforming vector can be decomposed as a Kronecker product of two smaller vectors. Such a decomposition leads to a joint optimization problem, which is then solved by using an alternating optimization strategy along with the steepest-descent method. The resulting algorithm, termed here Kronecker product constrained least-mean-square (KCLMS) algorithm, exhibits (in comparison to the well-known CLMS) improved convergence speed and reduced computational complexity; especially, for arrays with a large number of antennas. Simulation results are shown aiming to confirm the robustness of the proposed algorithm under different operating conditions.
ISSN:1051-2004
1095-4333
DOI:10.1016/j.dsp.2021.102968