LAUSR

Hybrid beamforming can provide rapid data transmission rates while reducing the complexity and cost of massive multiple-input multiple-output (MIMO) systems. However, channel state information (CSI) is imperfect in realistic downlink channels, introducing challenges to hybrid beamforming (HBF) design. This letter proposes an unsupervised deep learning neural network (USDNN) for hybrid beamforming to prevent the labeling overhead of supervised learning and improve the achievable sum rate based on imperfect CSI. The simulation results show that our proposed method is 74% better than MO and 120% better than orthogonal match pursuit (OMP) systems; our proposed USDNN can achieve near-optimal performance.