  Developing resource allocation algorithms with strong real-time and high
efficiency has been an imperative topic in wireless networks. Conventional
optimization-based iterative resource allocation algorithms often suffer from
slow convergence, especially for massive multiple-input-multiple-output (MIMO)
beamforming problems. This paper studies learning-based efficient massive
beamforming methods for multi-user MIMO networks. The considered massive
beamforming problem is challenging in two aspects. First, the beamforming
matrix to be learned is quite high-dimensional in case with a massive number of
antennas. Second, the objective is often time-varying and the solution space is
not fixed due to some communication requirements. All these challenges make
learning representation for massive beamforming an extremely difficult task. In
this paper, by exploiting the structure of the most popular WMMSE beamforming
solution, we propose convolutional massive beamforming neural networks (CMBNN)
using both supervised and unsupervised learning schemes with particular design
of network structure and input/output. Numerical results demonstrate the
efficacy of the proposed CMBNN in terms of running time and system throughput.
