A neural blind beamformer for cyclostationary signals

In this paper a blind beamforming algorithm based on a neural network is presented according to the characteristic of wireless communication signals. This method transforms the question of estimating beamformer weight vectors into the one of computing the SVD of the cross correlation matrix of array input signals and their frequency shift signals. A cross correlation neural network is introduced to compute the SVD of the cross correlation matrix so as to reduce the computational complexity and carry out the blind beamforming more efficiently. The improved cross-coupled Hebbian learning rule presented in this paper can make the weights of the neural network converge much fast. This method can restrain noise and interference. Simulation proves its correctness.