A Joint-Neural-Network-Based Channel Prediction for Millimeter-Wave Mobile Communications
Zihao Fu, Fei Du, Xiongwen Zhao, Suiyan Geng, Yu Zhang, Peng Qin
Abstract
Accurate prediction of millimeter-wave (mmWave) channel features can guarantee the quality of service in mmWave mobile communications. In this work, combining the advantages of deep neural network (DNN) and long short-term memory (LSTM), a novel approach is proposed to predict mmWave channel features. A joint prediction problem is raised that takes both the historical states of channel features and position information into account, while a novel DNN-LSTM structure is designed for realizing prediction. The proposed approach is validated by the mobile channel measurements conducted in a railway station and compared with existing approaches. The results show that the proposed DNN-LSTM based approach enables to predict both the change trend of channel features on large scale and the fluctuation on small scale, the accuracy can be improved by more than 4.5% compared with existing approaches.