A Novel Scatterer Density-Based Predictive Channel Model for 6G Wireless Communications

Artificial intelligence (AI) is a promising solution to achieve channel prediction under limited channel data. In this paper, a novel scatterer density-based predictive channel model is proposed to predict channels in multiple scenarios. By exploring the graph attention networks (GAT) and gated recurrent unit (GRU), the proposed model captures multi-domain information in dynamic scenarios. Besides, it extracts highly space-time correlated data characteristics, captures channel dynamic evolutional patterns, and predicts channels in different scenarios. The space-time graph channel datasets are constructed based on the ray tracing (RT) simulation channels. In the prediction experiments, the proposed method is validated on the datasets to predict channels with good performance. Compared with the 3GPP TR 38.901 channel model, the proposed model obtains more accurate channel statistical properties in different scenarios.