A novel deep learning based time-varying channel prediction method - Jie ZHANG; Lihua YANG; Zenghao WANG; Bo HU; Qian NIE (Dianxin kexue)
For high-speed mobile orthogonal frequency division multiplexing (OFDM) systems, a novel time-varying channel prediction method based on deep learning was proposed.To avoid the influence caused by random initialization of network parameters, the proposed method firstly obtains an ideal channel estimation based on data and pilot, and then pre-trains the back propagation (BP) neural network based on the channel estimation to obtain the ideal network initial parameters.Then, based on the initial network value obtained by pre-training, the proposed method uses the channel estimation based on pilot to train the BP neural network again.Finally, the proposed method realizes the single-time-and multi-time prediction of time-varying channels through on-line prediction.Simulation results show that the proposed method can significantly improve the prediction accuracy of time-varying channels and has a low computational complexity.