Multi-User Parallel Channel Extrapolation Based on Graph-Transformer

Traditional channel estimation method in massive MIMO system requires high overhead to maintain the accuracy in multiuser environments. To handle this problem, we propose the Graph-Transformer Multi-User Channel Extrapolation (G-TMUC) method, which exploits the spatial correlations between nearby users' channels. The model combines a Graph Neural Network (GNN) for location-based feature extraction and a Transformer for channel extrapolation. The G-TMUC model minimizes the need for frequent channel estimations by predicting the channels of nearby users based on known channel information, all while ensuring high accuracy. Simulation results demonstrate that G-TMUC achieves significant reductions in Normalized Mean Square Error (NMSE) and training overhead compared to existing methods, showing a reduction in pilot overhead by about 50% and NMSE improvement of more than 2.5 dB at low SNR regime.