Cooperative Wideband Spectrum Sensing for Cognitive Radio Devices Based on Uniform Sub-Nyquist Sampling in Sparse Domain

Nowadays, Cognitive Radio (CR) is projected as the technology that will maximize the utilization of the spectrum resources in next generation wireless systems. Therefore, the Spectrum Sensing (SS) is the key function, which allows CR to know the available spectrum resources of an interest band. Nevertheless, one of the major problems in the SS is the big amount of samples that are processed in the multiband signal sampling by Nyquist equal or higher rates, which generates big time detection, high energy consumption and the necessity of high processing capacity in Cognitive Radio Devices (CDR). Taking advantage of the spatial diversity in order to improve the development of the Wide Band Spectrum Sensing (WBSS), in this paper is proposed a cooperative algorithm of WBSS for CDR based on Sub-Nyquist sampling and matrix completion. Likewise, it is proposed an uniform sampling matrix for the multiband signal in the sparse domain, and in this wideband cooperative scenario there are obtained close expressions for detection probability, miss detection probability and false alarm probability. The simulation results show that the presented algorithm allows the improvement of the WBSS performance in terms of detection probability and the receiver's operational characteristics compared to other WBSS cooperative algorithms based on Sub-Nyquist sampling.