Adaptive Compressive Spectrum Sensing for Wideband Cognitive Radios
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.
Cite
@article{arxiv.1302.1842,
title = {Adaptive Compressive Spectrum Sensing for Wideband Cognitive Radios},
author = {Hongjian Sun and Wei-Yu Chiu and A. Nallanathan},
journal= {arXiv preprint arXiv:1302.1842},
year = {2013}
}
Comments
11 pages, 4 figures. This paper has been accepted to be published in IEEE Communications Letters. The associate editor coordinating the review of this letter and approving it for publication was O. Dobre