SINR analysis in persymmetric adaptive processing

Jun Liu, Hongwei Liu, Bo Chen, Xiang-Gen Xia

Abstract: We study the normalized output signal-to-interference-plus-noise ratio (SINR) of a sample matrix inversion (SMI) beamformer with exploiting a priori information on persymmetric structures in the received signal. An exact expression for the expectation of the normalized output SINR (i.e., average S-INR loss) of the persymmetric SMI beamformer is obtained. Simulation results reveal that the exploitation of the persymmetric structure is equivalent to doubling the amount of training data.