Spectrum Prediction via Graph Structure Learning

With the rapid development of machine learning technologies, data-driven spectrum prediction enables intelligent dynamic spectrum access to alleviate the bottleneck of spectrum resource scarcity and congestion. However, spectrum prediction still faces some key challenges, including how to exploit the implicit but crucial multi-band correlations in wideband spectrum data, and how to capture the temporal dynamics across different bands. Due to the ignorance of such crucial features inherent from spectrum occupancy patterns, existing learning-based spectrum prediction methods unfortunately suffer from inaccurate prediction performance. To fill this gap, this paper develops a novel model of graph convolutional regression neural network (GCRNN), by introducing efficient graph structure learning (GSL-GCRNN) for dynamic multi-band spectrum prediction. The proposed GSL-GCRNN model is designed to adaptively learn both the multi-band and temporal correlations in dynamic wideband spectrum scenarios. Empowered by the graph structure estimator, graph convolutional networks are fueled to effectively extract the correlations in the frequency domain, followed by gated recurrent unit networks to further extract the temporal correlations of each band. It is worth noting that the graph structure estimator further enables to learn the multi-band correlations across different time periods on-the-fly, enhancing the accuracy of wideband spectrum prediction in dynamic environments. Simulation results verify that our GSLGCRNN approach outperforms the benchmark methods.