A Graph Convolution Network Based Adaptive Cooperative Spectrum Sensing in Cognitive Radio Network

The hidden node problem is one of the most challenging issue in Cooperative Spectrum Sensing (CSS). The system models adopted by the existing Deep Learning-based spectrum sensing methods have not focused on modeling the hidden node scenario in cognitive radio networks. Further, these methods are unable to adapt to the dynamic channel conditions in the wireless environment since they have not considered the effect of fading environment. Motivated from these limitations, we propose GCN-CSS, a novel Graph Convolution Network (GCN) based cooperative spectrum sensing methodology which adapts to the dynamic changes in the Cognitive Radio Network. To the best of the author's knowledge, this is the first work to apply GCN for solving CSS problem. We have considered a practical system model which handles the dynamic channel condition i.e. SUs with multiple antennas experiencing different fading models with different fading severity. We have also catered the scenario of imperfect reporting channel between the SUs and the fusion centre along with the imperfect sensing channel to prove the robustness of the proposed model. With sufficient simulations, the superiority of the proposed methodology is proven in different dynamic scenarios of the wireless environment.

Automated summary

GCN-CSS is a novel Graph Convolution Network-based cooperative spectrum sensing methodology that adapts to the dynamic changes in the Cognitive Radio Network. The proposed model is proven to be superior in different dynamic scenarios of the wireless environment through sufficient simulations.