The Complex Convolutional Neural Network for Adaptive Spatio-Temporal Broadband Beamforming

Broadband beamforming technology is an effective solution in millimeter-wave (mmWave) massive multiple-input-multiple-output (MIMO) systems to counteract severe path loss via beamforming gains. One classic approach to implementing adaptive broadband beamforming is through spatio-temporal structure. In this paper, a method based on the complex convolutional neural network (CCNN) is proposed, namely, broadband beamforming prediction complex-valued network (BBPCNet), which can solve the performance degradation problem of traditional broadband beamforming methods under circumstance of limited snapshots. The BBPCNet leverages the CCNN to maintain the inherent amplitude and phase relationships of the complex-valued signals received by the array. A significant advantage of the proposed method is the elimination of the need for covariance matrix inversion to produce beamforming weights, resulting in lower computational complexity. Numerical simulations demonstrate effectiveness and superiority of the proposed method.