Compressive Spectrum Sensing Using Sampling-Controlled Block Orthogonal Matching Pursuit

This paper proposes two novel schemes of wideband compressive spectrum sensing (CSS) via block orthogonal matching pursuit (BOMP) algorithm, for achieving high accuracy in real time. These aim to reliably recover the by adaptively adjusting number required measurements without inducing unnecessary sampling redundancy. To this end, minimum successful recovery is first derived terms its probabilistic lower bound. Then, a CSS scheme proposed tightening bound, where key design nonlinear exponential indicator through general-purpose sampling-controlled algorithm (SCA). In particular, BOMP (SC-BOMP) developed holistic integration existing and SCA. For fast implementation, modified version SC-BOMP further exploring orthogonality form sub-coherence measurement matrices, which allows more smaller bound measurements. Such achieves desired tradeoff between complexity performance. Simulations demonstrate that outperform other benchmark algorithms.