  The great potentials of massive Multiple-Input Multiple-Output (MIMO) in
Frequency Division Duplex (FDD) mode can be fully exploited when the downlink
Channel State Information (CSI) is available at base stations. However, the
accurate CSI is difficult to obtain due to the large amount of feedback
overhead caused by massive antennas. In this paper, we propose a deep learning
based joint channel estimation and feedback framework, which comprehensively
realizes the estimation, compression, and reconstruction of downlink channels
in FDD massive MIMO systems. Two networks are constructed to perform estimation
and feedback explicitly and implicitly. The explicit network adopts a
multi-Signal-to-Noise-Ratios (SNRs) technique to obtain a single trained
channel estimation subnet that works well with different SNRs and employs a
deep residual network to reconstruct the channels, while the implicit network
directly compresses pilots and sends them back to reduce network parameters.
Quantization module is also designed to generate data-bearing bitstreams.
Simulation results show that the two proposed networks exhibit excellent
performance of reconstruction and are robust to different environments and
quantization errors.
