Adaptive Reduced-Dimensional Beamspace Beamformer Design by Analogue Beam Selection

Abstract

Adaptive beamforming of large antenna arrays is difficult to implement due to prohibitively high hardware cost and computational complexity. An antenna selection strategy was utilized to maximize the output signal-to-interference-plus- noise ratio (SINR) with fewer antennas by optimizing array configurations. However, antenna selection scheme exhibits high degradation in performance compared to the full array system. In this paper, we consider a reduced-dimensional beamspace beamformer, where analogue phase shifters adaptively synthesize a subset of orthogonal beams whose outputs are then processed in a beamspace beamformer. We examine the selection problem to adaptively identify the beams most relevant to achieving almost the full beamspace performance, especially in the generalized case without any prior information. Simulation results demonstrated that the beam selection enjoys the complexity advantages, while simultaneously enhancing the output SINR of antenna selection.