A Novel Spectrum Sensing Method in Cognitive Radio Networks Based on Graph Theory

In this research, a novel graph-theory-based spectrum sensing technique in cognitive radio (CR) networks is proposed. By mapping the received data to the graph-related datasets, and analyzing the corresponding adjacency matrix and the associate matrix, the traditional two hypotheses spectrum sensing problem can be changed to the classification problem in the graph theory. The computational complexity of the proposed approach is comparable with that of the covariance-based detection method in real applications. Computer simulation results verify the better performance of the proposed approach than the traditional methods.