Deep Learning-Based Overhead Minimizing Hybrid Beamforming for Wideband mmWave Systems

Hybrid beamforming (HBF) design for wideband millimeter-wave (mmWave) systems has two challenges: 1) design complexity due to frequency selectivity; 2) large pilot overhead and feedback overhead due to heavy dependence on the channel information. This letter proposes a deep learning-based HBF method for wideband mmWave systems. First, we simplify the complex HBF design problem into a network optimization problem by designing the loss function with spectral efficiency as an optimization objective. Then, we replace the instantaneous channel state information with channel statistics information of multiple time slots to achieve low system overhead. Specifically, we develop an HBF network (HBFNet) based on convolutional neural network with the channel covariance matrix (CCM) as input and the hybrid beamformer as output. Meanwhile, we design a constraint layer to satisfy the constant modulus constraint and power constraint in HBF design. In addition, we adopt the imperfect CCM for training to make the proposed method more robust.