  This letter studies deep learning (DL) approaches to optimize beamforming
vectors in downlink multi-user multi-antenna systems that can be universally
applied to arbitrarily given transmit power limitation at a base station. We
exploit the sum power budget as side information so that deep neural networks
(DNNs) can effectively learn the impact of the power constraint in the
beamforming optimization. Consequently, a single training process is sufficient
for the proposed universal DL approach, whereas conventional methods need to
train multiple DNNs for all possible power budget levels. Numerical results
demonstrate the effectiveness of the proposed DL methods over existing schemes.
