Cyclostationarity-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, the authors 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.