Sub-6GHz Aided Hybrid Beamforming for mmWave System

In this paper, we investigate the correlation between the sub-6GHz channel and the millimeter wave (mmWave) chan-nel, and then predict the mmWave downlink hybrid beamforming (HBF) matrices directly from the sub-6GHz uplink channel, based on a sophisticatedly designed deep learning architecture. Specifically, the neural network structure consists of three functional modules: the feature extraction module extracts channel features from a large amount of channel data, the feature fusion module combines multidimensional features, and the prediction module generates the HBF matrix. Moreover, we develop a power constraint module for digital domain and a constant modulus constraint module for analog domain to ensure that the output of the network satisfies the characteristics of HBF. Simulation results show that the proposed sub-6GHz assisted HBF algorithm without mmWave channel estimation saves 75% of channel state information compared to the method using mmWave pilot resources directly. Furthermore, to facilitate deployment, we design a low-complexity structure that achieves a remarkable reduction of 98.52% in parameters and 22.93% in computations.