Cyclostationary-based low complexity wideband spectrum sensing using compressive sampling

Detecting the presence of licensed users and avoiding interference to them is vital to the proper operation of a Cognitive Radio (CR) network. Operating in a wideband channel requires high Nyquist sampling rates, which is limited by the state-of-the-art A/D converters. Compressive sampling is a promising solution to reduce sampling rates required in modern wideband communication systems. Among various signal detectors, feature detectors which exploit a signal cyclostationarity are robust against noise uncertainties. In this paper, we exploit the sparsity of the two-dimensional spectral correlation function (SCF), and propose a reduced complexity reconstruction method of the Nyquist SCF from the sub-Nyquist samples. The reconstruction optimization is formulated as a regularized least squares problem, and its closed form solution is derived. We show that for a given spectrum sparsity, there exists a lower bound on sampling rates that allows reliable SCF reconstruction.