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A Kronecker product CLMS algorithm for adaptive beamforming.
- Source :
-
Digital Signal Processing . Apr2021, Vol. 111, pN.PAG-N.PAG. 1p. - Publication Year :
- 2021
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10512004
- Volume :
- 111
- Database :
- Academic Search Index
- Journal :
- Digital Signal Processing
- Publication Type :
- Periodical
- Accession number :
- 149127820
- Full Text :
- https://doi.org/10.1016/j.dsp.2021.102968