Robust Beamforming via Worst-Case SINR Maximization

Minimum variance beamforming, which uses a weight vector that maximizes the signal-to-interference-plus-noise ratio (SINR), is often sensitive to estimation error and uncertainty in the parameters, steering vector and covariance matrix. Robust beamforming attempts to systematically alleviate this se...

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Bibliographic Details
Published in:IEEE transactions on signal processing 2008-04, Vol.56 (4), p.1539-1547
Main Authors: Seung-Jean Kim, Magnani, A., Mutapcic, A., Boyd, S.P., Zhi-Quan Luo
Format: Article
Language:English
Subjects:
Additive noise
Algorithms
Applied sciences
Array signal processing
Beamforming
Cities and towns
Contracts
convex optimization
Covariance matrix
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Information, signal and communications theory
Interference
Mathematical analysis
Mathematical models
Maximization
Optimization
Programming
robust beamforming
Robustness
Sensor arrays
Signal and communications theory
Signal to noise ratio
Signal, noise
signal-to-interference-plus-noise ratio (SINR)
Studies
Telecommunications and information theory
Uncertainty
Vectors (mathematics)
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Summary:Minimum variance beamforming, which uses a weight vector that maximizes the signal-to-interference-plus-noise ratio (SINR), is often sensitive to estimation error and uncertainty in the parameters, steering vector and covariance matrix. Robust beamforming attempts to systematically alleviate this sensitivity by explicitly incorporating a data uncertainty model in the optimization problem. In this paper, we consider robust beamforming via worst-case SINR maximization, that is, the problem of finding a weight vector that maximizes the worst-case SINR over the uncertainty model. We show that with a general convex uncertainty model, the worst-case SINR maximization problem can be solved by using convex optimization. In particular, when the uncertainty model can be represented by linear matrix inequalities, the worst-case SINR maximization problem can be solved via semidefinite programming. The convex formulation result allows us to handle more general uncertainty models than prior work using a special form of uncertainty model. We illustrate the method with a numerical example.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2007.911498