GPAE-LSTMnet: A Novel Learning Structure for Mobile MIMO Channel Prediction

Mobile channel estimation is very challenging as usually it requires more pilots and channel observations to obtain the channel state information (CSI) and the resultant estimation accuracy may decrease with the number of antennas and sub-carriers. Through exploring the long-and-short-term intrinsic spatial and temporal correlation among a set of historic channel instances randomly obtained within a certain communication environment, channel prediction can help increase the CSI accuracy w.r.t. to that obtained from only the pilots, and thus save signaling overhead and computational cost. In this paper, we propose a novel generative Periodic-Activator-enabled Auto Encoder-LSTM network (GPAE-LSTMnet) for accurate channel prediction of mobile MIMO channels, which first compresses the high dimensional channel matrix with high-frequency features to a low dimensional space with relatively low-frequency feature space that has high data smoothness and is suitable for time-series sequence prediction. After that, a LSTM network is used to predict the channel in the low dimensional space, which ensures high accuracy and low computational cost. Experimental results show that our proposed learning structure outperforms existing methods especially when the dimension of CSI to be predicted is relatively high, the time interval of the CSI sequence is relatively long and the number of network parameters is highly limited.