  In order to achieve reliable communication with a high data rate of massive
multiple-input multiple-output (MIMO) systems in frequency division duplex
(FDD) mode, the estimated channel state information (CSI) at the receiver needs
to be fed back to the transmitter. However, the feedback overhead becomes
exorbitant with the increasing number of antennas. In this paper, a two stages
low rank (TSLR) CSI feedback scheme for millimeter wave (mmWave) massive MIMO
systems is proposed to reduce the feedback overhead based on model-driven deep
learning. Besides, we design a deep iterative neural network, named FISTA-Net,
by unfolding the fast iterative shrinkage thresholding algorithm (FISTA) to
achieve more efficient CSI feedback. Moreover, a shrinkage thresholding network
(ST-Net) is designed in FISTA-Net based on the attention mechanism, which can
choose the threshold adaptively. Simulation results show that the proposed TSLR
CSI feedback scheme and FISTA-Net outperform the existing algorithms in various
scenarios.
