Dual-Domain Adaptive Beamformer Under Linearly and Quadratically Constrained Minimum Variance

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In this paper, a novel adaptive beamforming algorithm is proposed under a linearly and quadratically constrained minimum variance (LQCMV) beamforming framework, based on a dual-domain projection approach that can efficiently implement a quadratic-inequality constraint with a possibly rank-deficient positive semi-definite matrix, and the properties of the proposed algorithm are analyzed. As an application, relaxed zero-forcing (RZF) beamforming is presented which adopts a specific quadratic constraint that bounds the power of residual interference in the beamformer output with the aid of interference-channel side-information available typically in wireless multiple-access systems. The dual-domain projection in this case plays a role in guiding the adaptive algorithm towards a better direction to minimize the interference and noise, leading to considerably faster convergence. The robustness issue against channel mismatch and ill-posedness is also addressed. Numerical examples show that the efficient use of interference side-information brings considerable gains.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2013-06
Language
English
Article Type
Article
Keywords

PROJECTED SUBGRADIENT METHOD; ARRAY

Citation

IEEE TRANSACTIONS ON SIGNAL PROCESSING, v.61, no.11, pp.2874 - 2886

ISSN
1053-587X
DOI
10.1109/TSP.2013.2254481
URI
http://hdl.handle.net/10203/174347
Appears in Collection
EE-Journal Papers(저널논문)
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