Multi-Dimensional Spectrum Prediction Using Closed-Form Continuous-Time Neural Network With Graph Attention

Spectrum prediction plays a pivotal role in the cognitive radio process, facilitating spectrum management and enhancing spectrum utilization. Due to the complexity and spatiotemporal correlation of spectrum data, spectrum prediction remains a challenging issue today. Building upon previous work, this paper addresses the insufficiency of spectrum spatial correlation in conventional prediction methods and proposes a novel spectrum prediction approach based on Closed-form Continuous-time Neural Network With Graph Attention (GA-CfC), which achieves precise prediction of multidimensional spectrum data. This method employs a Graph Attention Network (GAT) to extract spectral spatial correlations from data that has been preliminarily processed by convolutional layers across different frequency bands. The extracted features are then merged with the original spectrum data and fed into a CfC for iterative learning in the temporal dimension. Finally, a prediction is made based on temporal and spectral correlations using an attention mechanism-based hidden layer integration module. We conducted experiments on real-world data to validate the excellent performance of our method and compared it with other spectrum prediction methods. The experimental results demonstrate that our proposed method exhibits superior predictive performance.