A neural-network-based linearly constrained minimum variance beamformer

This paper presents a neural network approach for beam-forming and interference cancellation. A three-layer radial basis function neural network is trained with input-output pairs. The results obtained from this network are in excellent agreement with the Wiener solution. It was found that networks implementing these functions are successful in hacking mobile users in real time as they move across the antenna's field of view (C) 1999 John Wiley & Sons, Inc.