  Many performance gains achieved by massive multiple-input and multiple-output
depend on the accuracy of the downlink channel state information (CSI) at the
transmitter (base station), which is usually obtained by estimating at the
receiver (user terminal) and feeding back to the transmitter. The overhead of
CSI feedback occupies substantial uplink bandwidth resources, especially when
the number of the transmit antennas is large. Deep learning (DL)-based CSI
feedback refers to CSI compression and reconstruction by a DL-based autoencoder
and can greatly reduce feedback overhead. In this paper, a comprehensive
overview of state-of-the-art research on this topic is provided, beginning with
basic DL concepts widely used in CSI feedback and then categorizing and
describing some existing DL-based feedback works. The focus is on novel neural
network architectures and utilization of communication expert knowledge to
improve CSI feedback accuracy. Works on bit-level CSI feedback and joint design
of CSI feedback with other communication modules are also introduced, and some
practical issues, including training dataset collection, online training,
complexity, generalization, and standardization effect, are discussed. At the
end of the paper, some challenges and potential research directions associated
with DL-based CSI feedback in future wireless communication systems are
identified.
