  The potential advantages of intelligent wireless communications with
millimeter wave (mmWave) and massive multiple-input multiple-output (MIMO) are
based on the availability of instantaneous channel state information (CSI) at
the base station (BS). However, no existence of channel reciprocity leads to
the difficult acquisition of accurate CSI at the BS in frequency division
duplex (FDD) systems. Many researchers explored effective architectures based
on deep learning (DL) to solve this problem and proved the success of DL-based
solutions. However, existing schemes focused on the acquisition of complete CSI
while ignoring the beamforming and precoding operations. In this paper, we
propose an intelligent channel feedback architecture using eigenmatrix and
eigenvector feedback neural network (EMEVNet). With the help of the attention
mechanism, the proposed EMEVNet can be considered as a dual channel
auto-encoder, which is able to jointly encode the eigenmatrix and eigenvector
into codewords. Simulation results show great performance improvement and
robustness with extremely low overhead of the proposed EMEVNet method compared
with the traditional DL-based CSI feedback methods.
