  In multiple-input multiple-output (MIMO) systems, it is crucial of utilizing
the available channel state information (CSI) at the transmitter for precoding
to improve the performance of frequency division duplex (FDD) networks. One of
the mainchallenges is to compress a large amount of CSI in CSI feedback
transmission in massive MIMO systems. In this paper, we propose a deep learning
(DL)-based approach that uses a deep recurrent neural network (RNN) to learn
temporal correlation and adopts depthwise separable convolution to shrink the
model. The feature extraction module is also elaborately devised by
studyingdecoupled spatio-temporal feature representations in different
structures. Experimental results demonstrate that the proposed approach
outperforms existing DL-based methods in terms of recovery quality and
accuracy, which can also achieve remarkable robustness at low compression ratio
(CR).
