Wideband spectrum sensing is an essential functionality for cognitive radio networks. It enables cognitive radios to detect spectral holes over a wideband channel and to opportunistically use under-utilized frequency bands without causing harmful interference to primary networks. However, most of the work on wideband spectrum sensing presented in the literature employ the Nyquist sampling which requires very high sampling rates and acquisition costs. In this paper, a new wideband spectrum sensing algorithm based on compressed sensing theory is presented. The proposed method gives an effective sparse signal representation method for the wideband spectrum sensing problem. Thus, the presented method can effectively detect all spectral holes by finding the sparse coefficients. At the same time, the signal sampling rate and acquisition costs can be substantially reduced by using the compressive sampling technique. Simulation results testify the effectiveness of the proposed approach even in low signal-to-noise (SNR) cases.
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