Spectral Temporal Graph Neural Network for massive MIMO CSI Prediction
This addresses the need for accurate CSI prediction to optimize performance in 5G systems, offering incremental improvements over existing methods.
The paper tackles the problem of Channel State Information (CSI) prediction in 5G communication systems by introducing the Spectral-Temporal Graph Neural Network (STEM GNN), which improves sum rate by 11.9% over LSTM and 35% over RNN in one scenario.
In the realm of 5G communication systems, the accuracy of Channel State Information (CSI) prediction is vital for optimizing performance. This letter introduces a pioneering approach: the Spectral-Temporal Graph Neural Network (STEM GNN), which fuses spatial relationships and temporal dynamics of the wireless channel using the Graph Fourier Transform. We compare the STEM GNN approach with conventional Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) models for CSI prediction. Our findings reveal a significant enhancement in overall communication system performance through STEM GNNs. For instance, in one scenario, STEM GNN achieves a sum rate of 5.009 bps/Hz which is $11.9\%$ higher than that of LSTM and $35\%$ higher than that of RNN. The spectral-temporal analysis capabilities of STEM GNNs capture intricate patterns often overlooked by traditional models, offering improvements in beamforming, interference mitigation, and ultra-reliable low-latency communication (URLLC).