  Beamforming with large-scale antenna arrays has been widely used in recent
years, which is acknowledged as an important part in 5G and incoming 6G. Thus,
various techniques are leveraged to improve its performance, e.g., deep
learning, advanced optimization algorithms, etc. Although its performance in
many previous research scenarios with deep learning is quite attractive,
usually it drops rapidly when the environment or dataset is changed. Therefore,
designing effective beamforming network with strong robustness is an open issue
for the intelligent wireless communications. In this paper, we propose a robust
beamforming self-supervised network, and verify it in two kinds of different
datasets with various scenarios. Simulation results show that the proposed
self-supervised network with hybrid learning performs well in both classic
DeepMIMO and new WAIR-D dataset with the strong robustness under the various
environments. Also, we present the principle to explain the rationality of this
kind of hybrid learning, which is instructive to apply with more kinds of
datasets.
