Sub-6G Aided Millimeter Wave Hybrid Beamforming: A Two-Stage Deep Learning Framework With Statistical Channel Information

Abstract

This paper focuses on a deep learning (DL) framework for the Sub-6G aided millimeter-wave (mmWave) communication system, aiming to reduce the overhead of mmWave systems. The proposed framework consists of two-stage cascaded networks, named HestNet and HBFNet, for mmWave channel estimation and hybrid beamforming (HBF) design, respectively. The number of parameters for channel estimation is reduced by using channel covariance matrix (CCM) estimation instead. However, a new challenge of estimating high-dimensional data from low-dimensional data should be considered since the dimension of Sub-6G channel data is much smaller than that of mmWave. Subsequently, a data deformation approach is introduced into the framework to match the size of Sub-6G channel data with that of mmWave. The simulation results show that the application of statistical channel information based on Sub-6G channel information to aid mmWave communication is reasonable and effective, it achieves good estimation performance and spectral efficiency. Moreover, the two-stage cascaded network architecture proposed in this paper is also more robust to channel estimation errors.