  In this paper, we propose a new downlink beamforming strategy for mmWave
communications using uplink sub-6GHz channel information and a very few mmWave
pilots. Specifically, we design a novel dual-input neural network, called
FusionNet, to extract and exploit the features from sub-6GHz channel and a few
mmWave pilots to accurately predict mmWave beam. To further improve the
beamforming performance and avoid over-fitting, we develop two data
pre-processing approaches utilizing channel sparsity and data augmentation. The
simulation results demonstrate superior performance and robustness of the
proposed strategy compared to the existing one that purely relies on the
sub-6GHz information, especially in the low signal-to-noise ratio (SNR)
regions.
