Kernel-based nonlinear beamforming construction using orthogonal forward selection with the fisher ratio class separability measure

This letter shows that the wireless communication system capacity is greatly enhanced by employing nonlinear beamforming and that the optimal Bayesian beamformer outperforms the standard linear beamformer significantly in terms of a reduced bit error rate, at a cost of increased complexity. A block-data adaptive implementation of the Bayesian beamformer is realized based on an orthogonal forward selection procedure with the Fisher ratio for class separability measure.