A GAN-LSTM based AI Framework for 6G Wireless Channel Prediction

Compared with conventional passive channel modeling, artificial intelligence (AI) based channel models show great advantages in solving real-time prediction problems in wireless communications. In this paper, a generative adversarial network (GAN) and long short-term memory (LSTM) based channel prediction framework is proposed to model indoor wireless channels. By using GAN and LSTM, the model not only enriches the channel data but also achieves the sequence prediction, which can solve the problem of the shortage of training data and prediction channels in the space domain. The prediction performance is evaluated by comparing the root mean square error (RMSE) and mean absolute percentage error (MAPE) of measured data and predicted data. By comparing the statistical properties of the channel measurement data and of the synthetic data, it can be found that the proposed model can predict unknown information in the space domain.