CL-MFGCN: Graph Structure Contrastive Learning and Multiscale Feature Fusion Graph Convolutional Network for Spectrum Prediction

To address the conflict between the limited availability of spectrum resources and the swiftly growing number of frequency equipment in the Internet of Vehicles, this article starts from the solution of dynamic access of radio equipment and studies the problem of power spectrum density prediction of different channels, which is called spectrum prediction. We first propose the graph structure learning problem of electromagnetic spectrum data under the graph contrastive learning (GCL) framework from the causal perspective, and establishes a graph structure representation model between channel signal activity rules. Then, we establish the statistical distribution models of different channels at fine resolution based on the Gaussian mixture model. Then, the statistical model is embedded as prior knowledge using a graph convolutional network (GCN), and the channel association features are mapped to the association knowledge embedding using a graph structure encoder. This article proposes a novel spectrum prediction architecture based on GCL and multiscale feature fusion GCN (CL-MFGCN) to mine the time-frequency implicit knowledge of spectrum data, and the above knowledge embedding is integrated. This article visualizes the channel association relationship in the form of a graph structure. The experimental results indicate that the CL-MFGCN, reduces the average MAE by 14.6% (from 1.436 to 1.226) and the average MAPE by 12.9% (from 0.017 to 0.0148) compared to the second-best Pyraformer model, while maintaining a lower model complexity.