An Adaptive Compressive Wideband Spectrum Sensing Algorithm Based on Least Squares Support Vector Machine

Most of the compressive wideband spectrum sensing algorithms need to recover the spectrum, which require high computational complexity. Recently, a novel algorithm for compressive wideband sensing without spectrum recovery (NoR) was proposed. Its computational complexity is several orders of magnitude less than that of algorithms that need spectrum recovery. However, enabling by structure-constrained assumption of sparse spectrum, NoR may fail. In order to expand its scope of application while reducing the computational complexity as much as possible, we propose an adaptive sensing (ADP) algorithm that is a powerful hybrid of the no recovery and partial recovery (PR) algorithms. The ADP algorithm adaptively chooses the no recovery or partial recovery scheme depending on the situation learned by the least squares support vector machine (LS-SVM). By simulation and analysis, compared with NoR, PR and another excellent algorithm (orthogonal matching pursuit), the ADP suits better for practical applications.