Spectrum-sensing algorithm based on graph feature fusion

Graph-based spectrum sensing in noisy environments has major implications for civilian and military signal processing applications. However, existing algorithms suffer from high computational complexity and performance deterioration at low signal-to-noise ratios (SNRs). Therefore, a spectrum-sensing algorithm based on graph feature fusion using a quadratic form derived from self-loop weights and the graph Laplacian matrix is proposed in this study. The sum of the first and second block maxima of the power spectrum of the observed signal is selected as the input to the graph converter. Self-loop weights are combined with the Laplacian matrix to construct the graph quadratic form, which serves as the test statistic for decision-making. By applying majorisation and the extreme value theory, it is demonstrated that the proposed algorithm outperforms existing methods. The simulation results confirm the robust spectrum-sensing performance across various signal modulation types and pulse shapes. Thus, compared to existing algorithms, except block range- and energy-detection-based methods, the proposed algorithm demonstrates the best spectrum-sensing performance under low SNRs and channel-fading conditions, while achieving the lowest computational complexity. The proposed approach enables more efficient and accurate spectrum sensing, fostering advancements in communication technologies and defence applications.