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

FusionNet: Enhanced Beam Prediction for mmWave Communications Using Sub-6GHz Channel and A Few Pilots