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).

在 5G 通信系统领域，准确预测信道状态信息 (CSI) 对优化性能至关重要。该论文介绍了一种开创性的方法：采用图傅里叶变换，将无线信道的空间关系和时间动态融合到谱时图神经网络 (STEM GNN) 中。我们将 STEM GNN 方法与传统的循环神经网络 (RNN) 和长短期记忆 (LSTM) 模型进行了比较，发现 STEM GNN 显著提高了整体通信系统性能。例如，在一个场景中，STEM GNN 的速率为 5.009 bps/Hz，比 LSTM 高出 11.9%，比 RNN 高出 35%。STEM GNN 的谱时分析能力捕捉到传统模型常常忽视的复杂模式，提供了波束成形、干扰抑制和超可靠低延迟通信 (URLLC) 方面的改进。