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Compressed Edge Spectrum Sensing for Wideband Cognitive Radios

Free licensed spectral bands have become rare due to the increasing number of wireless users and their demand for high data rates. Likewise, the static allocation of these bands results in an under-utilization of the spectrum. Cognitive Radio (CR) has emerged as a promising solution to the dilemma by allowing opportunistic users to transmit in the absence of licensed users. Spectrum sensing is therefore the key component of CR and coexistence management in general. In order to detect as much transmission opportunities as possible, a large bandwidth has to be monitored which according to Shannon-Nyquist necessitates high sampling rates. For fast and accurate spectrum estimation, we propose a novel approach called Compressed Edge Spectrum Sensing (CESS) which exploits the sparsity of power spectrum edges and allows for sampling down to 6% of Nyquist without losses in the detection accuracy of occupied and unoccupied spectrum regions.