  Massive multiple-input multiple-output (MIMO) systems require downlink
channel state information (CSI) at the base station (BS) to achieve spatial
diversity and multiplexing gains. In a frequency division duplex (FDD)
multiuser massive MIMO network, each user needs to compress and feedback its
downlink CSI to the BS. The CSI overhead scales with the number of antennas,
users and subcarriers, and becomes a major bottleneck for the overall spectral
efficiency. In this paper, we propose a deep learning (DL)-based CSI
compression scheme, called DeepCMC, composed of convolutional layers followed
by quantization and entropy coding blocks. In comparison with previous DL-based
CSI reduction structures, DeepCMC proposes a novel fully-convolutional neural
network (NN) architecture, with residual layers at the decoder, and
incorporates quantization and entropy coding blocks into its design. DeepCMC is
trained to minimize a weighted rate-distortion cost, which enables a trade-off
between the CSI quality and its feedback overhead. Simulation results
demonstrate that DeepCMC outperforms the state of the art CSI compression
schemes in terms of the reconstruction quality of CSI for the same compression
rate. We also propose a distributed version of DeepCMC for a multi-user MIMO
scenario to encode and reconstruct the CSI from multiple users in a distributed
manner. Distributed DeepCMC not only utilizes the inherent CSI structures of a
single MIMO user for compression, but also benefits from the correlations among
the channel matrices of nearby users to further improve the performance in
comparison with DeepCMC. We also propose a reduced-complexity training method
for distributed DeepCMC, allowing to scale it to multiple users, and suggest a
cluster-based distributed DeepCMC approach for practical implementation.
