  We study a deep learning (DL) based limited feedback methods for
multi-antenna systems. Deep neural networks (DNNs) are introduced to replace an
end-to-end limited feedback procedure including pilot-aided channel training
process, channel codebook design, and beamforming vector selection. The DNNs
are trained to yield binary feedback information as well as an efficient
beamforming vector which maximizes the effective channel gain. Compared to
conventional limited feedback schemes, the proposed DL method shows an 1 dB
symbol error rate (SER) gain with reduced computational complexity.
