Graph Neural Network Based Cooperative Spectrum Sensing for Cognitive Radio

The rise of deep learning enables spectrum sensing to make decisions only based on the observed data which brings about a lot of flexibility. However, the existing data-driven spectrum detection methods handle the data in Euclidean space well but ignore the laten structure connections of the signal. To characterize this information, we propose a graph construction mechanism based on the similarity measure of the random signal and further convert the received signal into graph topology. On this basis, we propose a graph neural network based detector that consists of the construction of graph topology, offline training and online detection. The proposed data-driven method does not need any priori knowledge of the observed signal. Simulation results demonstrate that whether there is noise uncertainty or not, the proposed method is robust and performs better than the existing deep learning based methods.