Deep Learning Based Channel Prediction for Massive MIMO Systems in High-Speed Railway Scenarios

Abstract

This paper investigates the prediction model based on deep learning for the wireless channel characteristics of massive MIMO systems in high-speed railway (HSR) scenarios. Based on the propagation graph theory, we simulate the massive MIMO channel in a HSR cutting scenario. The datasets of spatial-temporal channel characteristics, involving channel state information, Ricean K-factor, delay spread, and angle spread, are generated for the model training and testing, and two kinds of prediction problem formulations, such as single-step and multi-steps, are designed. By considering both the spatial and temporal correlation properties in HSR massive MIMO channels, a novel channel prediction model that combines the convolutional long short-term memory (CLSTM) and convolutional neural network (CNN) is proposed and called as Conv-CLSTM. The hyperparameters of Conv-CLSTM are determined by comparative experiments and autocorrelation and similarity analysis. According to the performance evaluation, it is showed that the proposed Conv-CLSTM outperforms the other deep learning and machine learning models.