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