  The beamforming technology with large holographic antenna arrays is one of
the key enablers for the next generation of wireless systems, which can
significantly improve the spectral efficiency. However, the deployment of large
antenna arrays implies high algorithm complexity and resource overhead at both
receiver and transmitter ends. To address this issue, advanced technologies
such as artificial intelligence have been developed to reduce beamforming
overhead. Intuitively, if we can implement the near-optimal beamforming only
using a tiny subset of the all channel information, the overhead for channel
estimation and beamforming would be reduced significantly compared with the
traditional beamforming methods that usually need full channel information and
the inversion of large dimensional matrix. In light of this idea, we propose a
novel scheme that utilizes Wasserstein generative adversarial network with
gradient penalty to infer the full beamforming matrices based on very little of
channel information. Simulation results confirm that it can accomplish
comparable performance with the weighted minimum mean-square error algorithm,
while reducing the overhead by over 50%.
