Predecision for Wideband Spectrum Sensing With Sub-Nyquist Sampling

Built on compressed sensing theories, sub-Nyquist spectrum sensing (SNSS) has emerged as a promising solution to the wideband spectrum sensing problem. However, most of the existing SNSS methods do not distinguish if primary users (PUs) are present or absent in the concerned spectrum band and directly pursue support recovery of the PUs. This may lead to a high false alarm rate and a waste of computational cost. To address the issue, we propose a predecision algorithm, referred to as the pairwise channel energy ratio (PCER) detector, to determine the presence or absence of PUs prior to signal support recovery. The proposed detector is based on the popular modulated wideband converter (MWC) framework for SNSS, which has several advantages over other SNSS approaches. The PCER test statistic is constructed from compressed samples obtained by the MWC. The decision threshold and the detection probability are derived in closed form following the Neyman–Pearson criterion. Numerical results are presented to verify the theoretical calculation. The proposed PCER detection method is shown to be able to detect the existence of PUs in a wide range of signal-to-noise ratio, while being robust to noise uncertainty and does not need the prior knowledge of the PU signals. Additionally, our results show that the use of the PCER detector leads to a significant improvement of the correct support recovery rate of the PU signals.