  In a multiple-input multiple-output (MIMO) system, the availability of
channel state information (CSI) at the transmitter is essential for performance
improvement. Recent convolutional neural network (NN) based techniques show
competitive ability in realizing CSI compression and feedback. By introducing a
new NN architecture, we enhance the accuracy of quantized CSI feedback in MIMO
communications. The proposed NN architecture invokes a module named long
short-term memory (LSTM) which admits the NN to benefit from exploiting
temporal and frequency correlations of wireless channels. Compromising
performance with complexity, we further modify the NN architecture with a
significantly reduced number of parameters to be trained. Finally, experiments
show that the proposed NN architectures achieve better performance in terms of
both CSI compression and recovery accuracy.
