Compressive Spectrum Sensing Using Sampling-Controlled Block Orthogonal Matching Pursuit

TL;DR

This paper introduces adaptive sampling schemes using block orthogonal matching pursuit for wideband spectrum sensing, improving accuracy and reducing unnecessary measurements in real-time applications.

Contribution

It presents two novel sampling-controlled BOMP schemes that adaptively determine the minimum measurements needed for successful spectrum recovery, enhancing efficiency and accuracy.

Findings

The proposed schemes outperform benchmark algorithms in simulations.

A fast implementation balances complexity and performance.

The schemes adaptively adjust measurements based on probabilistic bounds.

Abstract

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.