Centralized sub-Nyquist wideband spectrum sensing for cognitive radio networks over fading channels

Abstract Currently, Cognitive Radio (CR) technology is expected to maximize the use of spectrum resources in next generation wireless systems. Hence, Spectrum Sensing (SS) is key to enable CR to gather knowledge from the spectral resources available in the studied bands. However, one of the main setbacks of SS is that it requires a large number of samples to be processed for multi-band signal sampling at rates close to the Nyquist rate. This increases overall detection time and energy consumption while it stimulates the need for high-processing capabilities in Cognitive Radio Devices (CRD). Taking advantage of spatial diversity to enhance the performance of Wide Band Spectrum Sensing (WBSS), this article proposes a new cooperative algorithm based on WBSS for CRD using Sub-Nyquist sampling and matrix completion. Furthermore, a uniform sampling matrix in the disperse domain of the multiband signal is presented. This cooperative wideband scenario leads to closed expressions for probabilities of detection, omitted detection and false alarm. The simulation results reveal that the algorithm enhances the performance of WBSS in terms of the detection probability, improving SNR by 5 dB. The proposed algorithm improves on computational complexity by a factor of at least log n in comparison to other WBSS cooperative algorithms based on Sub-Nyquist sampling.