Sub-Nyquist Spectrum Sensing Based on Modulated Wideband Converter in Cognitive Radio Sensor Networks

Abstract

The large-scale deployment of wireless sensor networks is indispensable to the success of Internet of Things. Considering dynamic spectrum access and the limited spectrum resources in cognitive radio sensor networks, sub-Nyquist spectrum sensing based on the modulated wideband converter is introduced. Since the transmission signals are usually modulated by different carrier frequencies, the interested spectrum can be modeled as the multiband signal. Modulated wideband converter (MWC) is an attractive alternative among several sub-Nyquist sampling systems because it has been implemented in practice and the frequency support reconstruction algorithm is the most important part in MWC. However, most existing reconstruction methods require the sparse information, which is difficult to acquire in practical scenarios. In this paper, we propose a blind multiband signal reconstruction method, referred to as the statistics multiple measurement vectors (MMV) iterative algorithm to bypasses the above problem. By exploiting the jointly sparse property of MMV model, the supports can be obtained by statistical analysis for the reconstruction results. Simulation results show that, without the sparse prior, the statistics MMV iterative algorithm can accurately determine the support of the multiband signal in a wide range of signal-to-noise ratio by using various numbers of sampling channels.