A Kronecker product CLMS algorithm for adaptive beamforming

被引：21

作者：

Kuhn, Eduardo Vinicius ^{[1
]}

Pitz, Ciro Andre ^{[2
]}

Matsuo, Marcos Vinicius ^{[2
]}

Bakri, Khaled Jamal ^{[3
]}

Seara, Rui ^{[3
]}

Benesty, Jacob ^{[4
]}

机构：

[1] Univ Tecnol Fed Parana, Dept Elect Engn, LAPSE Elect & Signal Proc Lab, BR-85902490 Toledo, Parana, Brazil

[2] Univ Fed Santa Catarina, Dept Control Automat & Computat Engn, GEPS Elect Signal Proc Grp, BR-89036004 Blumenau, SC, Brazil

[3] Univ Fed Santa Catarina, Dept Elect & Elect Engn, LINSE Circuits & Signal Proc Lab, BR-88040900 Florianopolis, SC, Brazil

[4] Univ Quebec, Energy Mat & Telecommun Res Ctr, INRS EMT Natl Inst Sci Res, Montreal, PQ H5A 1K6, Canada

来源：

DIGITAL SIGNAL PROCESSING | 2021年 / 111卷

关键词：

Adaptive beamforming; Antenna arrays; CLMS algorithm; Kronecker product; Mobile communications;

D O I：

10.1016/j.dsp.2021.102968

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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. (C) 2021 Elsevier Inc. All rights reserved.

引用

页数：8

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