Adaptive multistage beamforming of cellular signals using cyclic higher-order statistics

A new solution to blind beamforming for signals that exhibit higher-order cyclostationarity is presented. Exploiting some previous theoretical developments, we show how cyclic cumulants of the received signals can be used to obtain the weights of the beamformer that performs blind extraction. The method is based on a spatial interpretation of a deconvolution procedure known as the superexponential algorithm. The basic block processing algorithm is made fully adaptive using an adaptive total least squares scheme and applied to a typical mobile communications scenario where several cochannel interferers corrupt the signals of interest