Abstract
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.
Highlights
Compressed sensing (CS) was first used by Z
We have proposed a least squares support vector machine (LS-SVM) based adaptive compressive wideband spectrum sensing algorithm
The adaptive sensing (ADP) algorithm performs sensing on the folded spectrum provided by multicoset sampling
Summary
Compressed sensing (CS) was first used by Z. An artificial neural network (ANN)-based joint sensing algorithm combining energy detection and cyclostationary feature detection is proposed in [10]. We perform sensing on a folded spectrum model brought by multicoset sub-Nyquist sampling. If the number of signal samples for sensing is small and the signal-tonoise ratio (SNR) is low, the classification problem is linearly inseparable [11] In this situation, the performance of multiple hypothesis testing based energy detection will decrease seriously. The motivation of the proposed adaptive (ADP) algorithm is to address NoR’s problem of limited application scope and reduce the computational complexity as much as possible by adaptively choosing the NoR or PR scheme depending on the situation learned by the LS-SVM
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