  Coded caching provides significant gains over conventional uncoded caching by
creating multicasting opportunities among distinct requests.
  Massive multiple-input multiple-output (MIMO) systems require downlink
channel state information (CSI) at the base station (BS) to better utilize the
available spatial diversity and multiplexing gains. However, in a frequency
division duplex (FDD) massive MIMO system, the huge CSI feedback overhead
becomes restrictive and degrades the overall spectral efficiency. In this
paper, we propose a deep learning based channel state matrix compression
scheme, called DeepCMC, composed of convolutional layers followed by
quantization and entropy coding blocks. In comparison with previous works, the
main contributions of DeepCMC are two-fold: i) DeepCMC is fully convolutional,
and it can be used in a wide range of scenarios with various numbers of
sub-channels and transmit antennas; ii) DeepCMC includes quantization and
entropy coding blocks and minimizes a cost function that accounts for both the
rate of compression and the reconstruction quality of the channel matrix at the
BS. Simulation results demonstrate that DeepCMC significantly outperforms the
state of the art compression schemes in terms of the reconstruction quality of
the channel state matrix for the same compression rate, measured in bits per
channel dimension.
