Reliable and Efficient Sub-Nyquist Wideband Spectrum Sensing in Cooperative Cognitive Radio Networks

The rising popularity of wireless services resulting in spectrum shortage has motivated dynamic spectrum sharing to facilitate efficient usage of the underutilized spectrum. Wideband spectrum sensing is a critical functionality to enable dynamic spectrum access by enhancing the opportunities of exploring spectral holes, but entails a major implementation challenge in compact commodity radios that only have limited energy and computation capabilities. In contrast to the traditional sub-Nyquist approaches where a wideband signal or its power spectrum is first reconstructed from compressed samples, this paper proposes a sub-Nyquist wideband spectrum sensing scheme that locates occupied channels blindly by recovering the signal support, based on the jointly sparse nature of multiband signals. Exploiting the common signal support shared among multiple secondary users (SUs), an efficient cooperative spectrum sensing scheme is developed, in which the energy consumption on wideband signal acquisition, processing, and transmission is reduced with detection performance guarantee. Based on subspace decomposition, the low-dimensional measurement matrix, computed at each SU from local sub-Nyquist samples, is deployed to reduce the transmission and computation overhead while improving noise robustness. The theoretical analysis of the proposed sub-Nyquist wideband sensing algorithm is derived and verified by numerical analysis and further tested on real-world TV white space signals. It shows that the proposed scheme can achieve good detection performance as well as reduce the computation and implementation complexity, in comparison with the conventional cooperative wideband spectrum sensing schemes.