Non-Gaussianity testing based robust compressive wideband spectrum sensing in CR networks

In cognitive radio networks, secondary users (SUs) need to sense/detect the vacant spectrum holes in a wide frequency band for achieve the dynamic spectrum access. The combination of compressive sensing (CS) with wavelet edge detection makes the wideband spectrum reconstruction and detection to be feasible. The existence of small-scale primary users (SSPUs) such as wireless microphones and mobile devices, however, brings a difficulty for the wideband spectrum sensing of SUs mainly due to the SSPUs´ weak signal strengths and low singal-noise-ratio (SNR) conditions. To solve this probelm, a novel robust compressive wideband spectrum sensing algorithm is proposed in this paper by exploiting the non-Gaussianity properties of the SSPUs´ spectrum and utilizing the non-Gaussianity test. Since the spectrum of a SU´s received signal theoretically follows different statistical distribution when the primary users (PUs) are present or not, the proposed algorithm uses the Pearson´s Chi-squared test statistic to measure the non-Gaussianity properties of the compressive reconstructed wideband spectrum, then find out the vacant spectrum regions. Simulation results show that our algorithm can obtain a good wideband spectrum sensing performance even for the SSPUs under low SNRs in cognitive wireless networks.