Channel Estimation Method Based on Deep Learning in High-Speed Mobile Environments

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. © 2019, Chinese Institute of Electronics. All right reserved.