Shrinkage Widely Linear Recursive Least Square Algorithms for Beamforming
Summary :
Shrinkage widely linear recursive least squares (SWL-RLS) and its improved version called structured shrinkage widely linear recursive least squares (SSWL-RLS) algorithms are proposed in this paper. By using the relationship between the noise-free a posterior and a priori error signals, the optimal forgetting factor can be obtained at each snapshot. In the implementation of algorithms, due to the a priori error signal known, we still need the information about the noise-free a priori error which can be estimated with a known formula. Simulation results illustrate that the proposed algorithms have faster convergence and better tracking capability than augmented RLS (A-RLS), augmented least mean square (A-LMS) and SWL-LMS algorithms.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E99-B No.7 pp.1532-1540
- Publication Date
- 2016/07/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2015EBP3322
- Type of Manuscript
- PAPER
- Category
- Antennas and Propagation
Authors
Huaming QIAN
Harbin Engineering University
Ke LIU
Harbin Engineering University
Wei WANG
Harbin Engineering University
Keyword
Latest Issue
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Huaming QIAN, Ke LIU, Wei WANG, "Shrinkage Widely Linear Recursive Least Square Algorithms for Beamforming" in IEICE TRANSACTIONS on Communications,
vol. E99-B, no. 7, pp. 1532-1540, July 2016, doi: 10.1587/transcom.2015EBP3322.
Abstract: Shrinkage widely linear recursive least squares (SWL-RLS) and its improved version called structured shrinkage widely linear recursive least squares (SSWL-RLS) algorithms are proposed in this paper. By using the relationship between the noise-free a posterior and a priori error signals, the optimal forgetting factor can be obtained at each snapshot. In the implementation of algorithms, due to the a priori error signal known, we still need the information about the noise-free a priori error which can be estimated with a known formula. Simulation results illustrate that the proposed algorithms have faster convergence and better tracking capability than augmented RLS (A-RLS), augmented least mean square (A-LMS) and SWL-LMS algorithms.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2015EBP3322/_p
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@ARTICLE{e99-b_7_1532,
author={Huaming QIAN, Ke LIU, Wei WANG, },
journal={IEICE TRANSACTIONS on Communications},
title={Shrinkage Widely Linear Recursive Least Square Algorithms for Beamforming},
year={2016},
volume={E99-B},
number={7},
pages={1532-1540},
abstract={Shrinkage widely linear recursive least squares (SWL-RLS) and its improved version called structured shrinkage widely linear recursive least squares (SSWL-RLS) algorithms are proposed in this paper. By using the relationship between the noise-free a posterior and a priori error signals, the optimal forgetting factor can be obtained at each snapshot. In the implementation of algorithms, due to the a priori error signal known, we still need the information about the noise-free a priori error which can be estimated with a known formula. Simulation results illustrate that the proposed algorithms have faster convergence and better tracking capability than augmented RLS (A-RLS), augmented least mean square (A-LMS) and SWL-LMS algorithms.},
keywords={},
doi={10.1587/transcom.2015EBP3322},
ISSN={1745-1345},
month={July},}
Copy
TY - JOUR
TI - Shrinkage Widely Linear Recursive Least Square Algorithms for Beamforming
T2 - IEICE TRANSACTIONS on Communications
SP - 1532
EP - 1540
AU - Huaming QIAN
AU - Ke LIU
AU - Wei WANG
PY - 2016
DO - 10.1587/transcom.2015EBP3322
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E99-B
IS - 7
JA - IEICE TRANSACTIONS on Communications
Y1 - July 2016
AB - Shrinkage widely linear recursive least squares (SWL-RLS) and its improved version called structured shrinkage widely linear recursive least squares (SSWL-RLS) algorithms are proposed in this paper. By using the relationship between the noise-free a posterior and a priori error signals, the optimal forgetting factor can be obtained at each snapshot. In the implementation of algorithms, due to the a priori error signal known, we still need the information about the noise-free a priori error which can be estimated with a known formula. Simulation results illustrate that the proposed algorithms have faster convergence and better tracking capability than augmented RLS (A-RLS), augmented least mean square (A-LMS) and SWL-LMS algorithms.
ER -
English
