Subspace-Aided Low-Complexity Blind Compressive Spectrum Sensing over TV Whitespace

Compressive sensing (CS) techniques have been proposed for wideband spectrum sensing applications to achieve sub-Nyquist-rate sampling. The complexity of CS recovery algorithm and the detection performance against noise are two of the main challenges of the implementation of compressive spectrum sensing (CSS). We hereby propose CSS scheme based on orthogonal matching pursuit (OMP) with the aid of spectrum sparsity order estimation enabled by detecting the signal subspace dimensionality directly from sub-Nyquist measurements. The computational effort of spectrum recovery can be saved superlinearly with the reduction of iterations. With the estimated spectrum sparsity order, the OMP algorithm is proposed to run only an explicit and a fraction of iterations compared to the cases where such estimation is absent. Besides, the estimation of active channel number also enables blind and hard decision of channel occupancy where threshold adaption for energy detection is avoided. Moreover, the detection performance of the proposed CSS scheme by simulation shows superior robustness against noise compared to the energy detection scheme.