Adaptive Compressive Spectrum Sensing for Wideband Cognitive Radios

TL;DR

This paper introduces an adaptive spectrum sensing algorithm for wideband cognitive radios that uses compressed sensing and an l2 norm validation to improve sensing efficiency and throughput.

Contribution

It proposes a novel adaptive spectrum sensing method combining compressed sensing with an l2 norm validation for automatic termination.

Findings

Reduces spectrum sensing time significantly

Enhances cognitive radio throughput

Effective in reconstructing wideband spectrum from compressed samples

Abstract

This letter presents an adaptive spectrum sensing algorithm that detects wideband spectrum using sub-Nyquist sampling rates. By taking advantage of compressed sensing (CS), the proposed algorithm reconstructs the wideband spectrum from compressed samples. Furthermore, an l2 norm validation approach is proposed that enables cognitive radios (CRs) to automatically terminate the signal acquisition once the current spectral recovery is satisfactory, leading to enhanced CR throughput. Numerical results show that the proposed algorithm can not only shorten the spectrum sensing interval, but also improve the throughput of wideband CRs.