Efficient Blind Cooperative Wideband Spectrum Sensing Based on Joint Sparsity

Wideband spectrum sensing is a critical functionality in cognitive radio networks to enable dynamic spectrum sharing, but entails a major implementation challenge in compact commodity radios with restricted energy and computation capabilities. Exploiting jointly sparse nature of multiband signals, this paper proposes an efficient blind sub-Nyquist cooperative wideband spectrum sensing scheme, which reduces energy consumption in wideband signal acquisition, processing and transmission, with performance guarantee. In contrast to traditional sub-Nyquist approaches where a wideband signal or its power spectrum is first reconstructed from compressed samples, the proposed scheme locates occupied channels by recovering the signal support jointly from multiple secondary user (SU) measurements. Based on subspace decomposition, the low-dimensional measurement matrix computed at each SU from local sub-Nyquist samples can reduce transmission overhead while improving noise robustness. Numerical analysis and simulation results show that the proposed scheme can achieve good detection performance as well as reduce computation and implementation complexity in comparison with conventional cooperative wideband spectrum sensing schemes.