SCM-GNN: A Graph Neural Network-Based Multi-Antenna Spectrum Sensing in Cognitive Radio

Abstract

Spectrum Sensing plays a crucial role in cognitive radio and serves as a fundamental requirement for achieving dynamic spectrum access. This work investigates a novel multi-antenna spectrum sensing framework based on graph neural networks to accurately identify the state of primary users. Specifically, the work proposes a graph spectral convolution-based spectrum sensing scheme (SCM-GNN), which employs stacked graph convolutions to capture the dependencies contained in test statistics. To further enhance the detection performance of SCM-GNN, the work introduces a covariance matrix with smooth factor as the test statistic. The covariance matrix includes more discriminative information and assists the SCM-GNN in achieving state-of-the-art detection performance. Simulation results demonstrate that the proposed algorithm outperforms existing works in terms of detection performance under the influence of various non-ideal factors, such as general Gaussian noise, channel fading, large-scale fading, real-world scenario, and imperfect reporting channel.