Higher-Order Cumulant-Assisted Constant Wideband Compressive Spectrum Sensing Using Semantic Correlation Mining

Since the waveforms of the signals received at adjacent times are different due to varying modulated signals, it is still a key challenge on how to describe temporal correlations precisely in compressive spectrum sensing (CSS). In this paper, we propose a higher-order cumulant-assisted CSS (HOC-CSS) algorithm, where the semantic correlation of adjacent received signals in higher-order domain is exploited. Firstly, the fourth-order cumulants of the reconstructed signals in the previous time are calculated. Secondly, the fourth-order cumulants are integrated with $\ell _{p}$ -norm minimization to estimate the current spectrum signals. Furthermore, a two-branch CSS scheme is proposed to eliminate the effect causing by adjacent signals under different modulations. The HOC-CSS is performed in one branch and a noninformative CSS algorithm is performed in another branch, thereby two different reconstructed signals are obtained. The Euclidean distances between the measurements and the projection of reconstructed signals are calculated. The final output is the estimated signal with smaller Euclidean distance. The experimental results demonstrate that the proposed algorithm has more than 8% and 10% detection performance improvement at false alarm probability 0.1, under compressive ratio of 0.4 and 0.5 respectively, compared with the state-of-the-art CSS schemes, e.g., HMCPI-CSS, Weighted $\ell _{1}$ -CSS, AR-IRLS, and IP-IRLS.