A class of constant modulus algorithms for uniform linear arrays with a conjugate symmetric constraint

A class of constant modulus algorithms (CMAs) subject to a conjugate symmetric constraint is proposed for blind beamforming based on the uniform linear array structure. The constraint is derived from the beamformer with an optimum output signal-to-interference-plus-noise ratio (SINR). The effect of...

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Veröffentlicht in:Signal processing Jg. 90; H. 9; S. 2760 - 2765
Hauptverfasser: Zhang, Lei, Liu, Wei, Langley, Richard J.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 01.09.2010
Elsevier
Schlagworte:
Algorithms
Applied sciences
Blind beamforming
Conjugate symmetric
Conjugates
Constant modulus
Detection, estimation, filtering, equalization, prediction
Equivalence
Exact sciences and technology
Information, signal and communications theory
Least squares method
Linear arrays
Mathematical analysis
Optimization
Signal and communications theory
Signal, noise
Telecommunications and information theory
Uniform linear arrays
Weight reduction
Blind beamforming
Conjugate symmetric
Constant modulus
Uniform linear arrays
Performance evaluation
Adaptive algorithm
Updating
Output signal
Beam forming
Constant modulus algorithm
Least squares method
Linear antenna arrays
Signal to interference plus noise ratio
ISSN:0165-1684, 1872-7557
Online-Zugang:Volltext
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Zusammenfassung:A class of constant modulus algorithms (CMAs) subject to a conjugate symmetric constraint is proposed for blind beamforming based on the uniform linear array structure. The constraint is derived from the beamformer with an optimum output signal-to-interference-plus-noise ratio (SINR). The effect of the additional constraint is equivalent to adding a second step to the original adaptive algorithms. The proposed approach is general and can be applied to both the traditional CMA and its all kinds of variants, such as the linearly constrained CMA (LCCMA) and the least squares CMA (LSCMA) as two examples. With this constraint, the modified CMAs will always generate a weight vector in the desired form for each update and the number of adaptive variables is effectively reduced by half, leading to a much improved overall performance.
Bibliographie:ObjectType-Article-2
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ISSN:0165-1684
1872-7557
DOI:10.1016/j.sigpro.2010.04.003