Abstract
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.
Original language | English (US) |
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Pages (from-to) | 1539-1547 |
Number of pages | 9 |
Journal | IEEE Transactions on Signal Processing |
Volume | 56 |
Issue number | 4 |
DOIs | |
State | Published - Apr 1 2008 |
Keywords
- Beamforming
- Convex optimization
- Robust beamforming
- Signal-to-interference-plus-noise ratio (SINR)