Low-Complexity Subspace-Aided Compressive Spectrum Sensing over Wideband 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). Greedy algorithms have been of particular interest in CSS due to low complexity. We firstly propose a novel spectrum sparsity estimation scheme directly from sub-Nyquist measurements, with which the computational effort of greedy pursuit algorithms can be saved and recovery performance improved. Besides, the spectrum sparsity estimates also enable hard detection of channel occupancy where threshold adaption for energy detection is avoided. Moreover, with the detected dimension of signal subspace, we propose to implement joint-block-sparse multiple-measurement-vector (MMV) model of CSS whose dimension can be reduced to minimum and meanwhile a large portion of noise is removed. The proposed MMV model with noise and dimension reduction further improves the detection performance and also keeps the complexity low. Finally, we generalize the hard thresholding pursuit (HTP) algorithm to recover joint-block-sparse signals. In simulations, the detection performance and complexity of the proposed CSS scheme show striking superiority against multiple benchmarking schemes.