Robust compressive wideband spectrum sensing based on non-Gaussianity test

In cognitive radio networks (CRNs), wideband spectrum sensing is an important task for secondary users (SUs) to achieve the dynamic spectrum access. Even though the wideband spectrum detection is feasible by combining the compressive sensing (CS) with wavelet transform, however, accurate detection of the small-scale primary users (SSPUs) such as wireless microphones and mobile devices is still difficult especially under low signal-noise-ratio (SNR) conditions due to the SSPU's weak signal strength. To cope with this challenge, we propose a novel robust compressive wideband spectrum sensing algorithm by exploiting the non-Gaussianity properties of the SSPU's spectrum. Since the spectrum of received signal at SUs more closely approximate the Gaussian distribution when the primary users (PUs) are absent than that of the PU's spectrum, it is possible to design a test statistic to measure the non-Gaussianity properties of the wideband spectrum reconstructed by CS method, then make a decision on whether there are vacant frequency bands. Simulation results show the effectiveness of the proposed algorithm even.