Environment information-based channel prediction method assisted by graph neural network

Recently, whether the channel prediction can be achieved in diverse communication scenarios by directly utilizing the environment information gained lots of attention due to the environment impacting the propagation characteristics of the wireless channel. This paper presents an environment information-based channel prediction (EICP) method for connecting the environment with the channel assisted by the graph neural networks (GNN). Firstly, the effective scatterers (ESs) producing paths and the primary scatterers (PSs) generating single propagation paths are detected by building the scatterer-centered communication environment graphs (SC-CEGs), which can simultaneously preserve the structure information and highlight the pending scatterer. The GNN-based classification model is implemented to distinguish ESs and PSs from other scatterers. Secondly, large-scale parameters (LSP) and small-scale parameters (SSP) are predicted by employing the GNNs with multi-target architecture and the graphs of detected ESs and PSs. Simulation results show that the average normalized mean squared error (NMSE) of LSP and SSP predictions are 0.12 and 0.008, which outperforms the methods of linear data learning.