Adaptive constrained learning in reproducing Kernel Hilbert spaces: the robust beamforming case
IEEE Transactions on Signal Processing
Fixed point optimization algorithm and its application to network bandwidth allocation
Journal of Computational and Applied Mathematics
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This paper uses the hybrid steepest descent method (HSDM) to design robust smart antennas. Several design criteria as well as robustness are mathematically described by a finite collection of closed convex sets in a real Euclidean space. Desirable beamformers are defined as points of the generalized convex feasible set which is well defined even in the case of inconsistent design criteria. A quadratic cost function is formed by the correlations of the incoming data, and the HSDM constructs a point sequence that (strongly) converges to the (unique) minimizer of the cost function over the generalized convex feasible set. Numerical examples validate the proposed design.