Fast Time-Varying Wireless Channel Prediction Based on Deep Learning

This paper presents a channel prediction method based on deep temporal convolutional neural networks to accurately obtain channel information between different time slots. This method uses causal convolution and dilated convolution to capture the temporal correlations of historical Channel State Information (CSI) for predicting future CSI. The proposed method is evaluated using both original channel matrix coefficients generated by the simulation platform and measured Channel Quality Indicator (CQI) data as the CSI datasets, demonstrating its performance in both scenarios. Simulation results show that the proposed method outperforms the non-prediction and mainstream prediction methods, exhibiting superior prediction performance and adaptability to real-world environments.