Existing channel state information (CSI) feedback methods for frequency division duplexing (FDD) multiple-input multiple-output (MIMO) systems have high complexity and low feedback accuracy. In this paper

a deep learning-based CSI compression feedback method is proposed. The method first uses the convolutional neural network (CNN) to extract the channel feature vector

and then uses the maximum pooling (Maxpooling) network to compress the CSI. Finally

considering the spatial correlation of the massive MIMO channel

bidirectional long short-term memory (Bi-LSTM) network and bidirectional convolution long-term memory (Bi-ConvLSTM) network are used for single-user and multi-user scenarios respectively to recover the CSI. In this paper

the deep learning network is trained offline using massive MIMO channel data

the channel information learned by the network can fully characterize the states of the channel. The simulation results show that compared with the existing typical CSI feedback methods

the proposed method has higher feedback accuracy

shorter running time and better system performance.