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 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 usi...

• Type: Journal
• Year: 2008
• Where: TSP
• Authors: Seung-Jean Kim, Alessandro Magnani, Almir Mutapcic, Stephen P. Boyd, Zhi-Quan Luo
• Keywords: SINR Maximization Problem, TSP 2008, Uncertainty Model, Worst-case Sinr Maximization