A-GCRNN: Attention Graph Convolution Recurrent Neural Network for Multi-Band Spectrum Prediction

Radio spectrum prediction is an important task for dynamic spectrum management and spectrum congestion mitigation. However, due to the complexity and spatiotemporal variability of spectrum data, spectrum prediction has always been a challenging problem. To improve prediction accuracy, this paper proposes a multi-band spectrum prediction method based on attention graph convolutional recurrent neural networks (A-GCRNN), which applies temporal correlation and frequency band correlation to spectrum prediction tasks. This method represents the spectrum data of multiple bands as a graph, with each band corresponding to a node in the graph, and utilizes graph convolutional networks (GCNs) to learn the correlations between different bands. Then, a gated recurrent unit (GRU) network is employed to capture temporal correlations for each band, thereby fusing the acquired feature information to predict the future spectrum of each frequency band. In addition, an attention network is used to weight the output hidden states and further enhance the model's convergence rate. We conduct experiments on a real-world spectrum dataset and compare our method with other spectrum prediction methods. The results demonstrate that our approach achieves better performance in multi-band spectrum prediction tasks.