The AI-Based Channel Prediction Scheme for the 5G Wireless System in High-Speed V2I Scenarios

Abstract

Now, the fifth-generation (5G) system plays a more and more important role in the high-speed vehicle-to-infrastructure (V2I) scenario. In order to realize the high-reliability and high-efficiency transmission, it is essential to obtain accurate channel state information (CSI) for the 5G system. However, due to the fast time-varying and nonstationary characteristics of the channel in high-speed V2I scenarios, channel estimation is a challenging issue. In this paper, an artificial intelligence- (AI-) based channel prediction scheme, called AI-ChannelNet, is proposed to improve the CSI prediction performance in high-speed V2I scenarios. Specifically, AI-ChannelNet is trained in real time based on the historical channel estimation on the reference signal (RS) to realize accurate channel prediction and then recovers the received signal according to the predicted channel information. The integration of the convolutional neural network (CNN) and long short-term memory (LSTM) is designed to extract temporal features of the channel. And an online RS-based training algorithm is proposed, enabling AI-ChannelNet to track the channel variation. Evaluated by experiments, the proposed scheme outperforms conventional methods a lot, and more improvement could be achieved at a higher speed. Besides, the proposed scheme performs well without modification of the 5G radio frame and loss of transmission efficiency.