Aiming at the problem that the downlink channel estimation performance is limited due to the fast time-varying and non-stationary characteristics in the high-speed mobile environment

this paper proposes a channel estimation network based on deep learning

called ChanEstNet. ChanEstNet uses the convolutional neural network (CNN) to extract channel response feature vectors and recurrent neural network (RNN) for channel estimation.We use the standard high-speed channel data to conduct offline training for the learning network

fully excavate the channel information in the training sample

make it learn the characteristics of fast time-varying and non-stationary channels in high-speed mobile environments

and better track the characteristics of channel changing in high-speed environment. The simulation results show that in the high-speed mobile environment

compared with the traditional methods

the proposed channel estimation method has low computational complexity and significant performance improvement.