  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, CSI feedback overhead degrades the
overall spectral efficiency. Convolutional neural network (CNN)-based CSI
feedback compression schemes has received a lot of attention recently due to
significant improvements in compression efficiency; however, they still require
reliable feedback links to convey the compressed CSI information to the BS.
Instead, we propose here a CNN-based analog feedback scheme, called
AnalogDeepCMC, which directly maps the downlink CSI to uplink channel input.
Corresponding noisy channel outputs are used by another CNN to reconstruct the
DL channel estimate. Not only the proposed outperforms existing digital CSI
feedback schemes in terms of the achievable downlink rate, but also simplifies
the operation as it does not require explicit quantization, coding and
modulation, and provides a low-latency alternative particularly in rapidly
changing MIMO channels, where the CSI needs to be estimated and fed back
periodically.
