  Massive multiple-input multiple-output can obtain more performance gain by
exploiting the downlink channel state information (CSI) at the base station
(BS). Therefore, studying CSI feedback with limited communication resources in
frequency-division duplexing systems is of great importance. Recently, deep
learning (DL)-based CSI feedback has shown considerable potential. However, the
existing DL-based explicit feedback schemes are difficult to deploy because
current fifth-generation mobile communication protocols and systems are
designed based on an implicit feedback mechanism. In this paper, we propose a
DL-based implicit feedback architecture to inherit the low-overhead
characteristic, which uses neural networks (NNs) to replace the precoding
matrix indicator (PMI) encoding and decoding modules. By using environment
information, the NNs can achieve a more refined mapping between the precoding
matrix and the PMI compared with codebooks. The correlation between subbands is
also used to further improve the feedback performance. Simulation results show
that, for a single resource block (RB), the proposed architecture can save
25.0% and 40.0% of overhead compared with Type I codebook under two antenna
configurations, respectively. For a wideband system with 52 RBs, overhead can
be saved by 30.7% and 48.0% compared with Type II codebook when ignoring and
considering extracting subband correlation, respectively.
