A Full-blind Sub-Nyquist Sampling Method for Wideband Spectrum Sensing

Sub-Nyquist sampling is an effective approach to mitigate the high sampling rate pressure for wideband spectrum sensing. The existing sub-Nyquist sampling method requires excessive large measurement matrix and exact sparsity level in recovery phase. Considering this problem, a method of applying Modulated Wideband Converter (MWC) with small measurement matrix to wideband spectrum sensing is proposed. An improved sufficient condition for spectrum-blind recovery based on the redefinition of spectrum sparse signal model is presented, which breaks the dependence on the maximum width of bands for MWC construction. In recovery phase, the Sparsity Adaptive Matching Pursuit (SAMP) algorithm is introduced to Multiple Measurement Vector (MMV) problem. As a result, a full-blind low rate sampling method requiring neither the maximum width nor the exact number of bands is implemented. The experimental results verify the effectiveness of the proposed method.