Performance Evaluation of Adaptive Compressed Spectrum Sensing for Wideband Cognitive Radio Networks

In cognitive radio networks, spectrum sensing is one of the most challenging technologies. However, traditional spectrum sensing methods need too much detection time in a very low SNR environment, and the detection probability is rather small. Compressive sensing provides a new and attractive perspective for spectrum sensing in cognitive radio network. Considering different influences of SNRs, a modified spectrum sensing algorithm based on compressive sensing is proposed in this paper. In order to achieve a good tradeoff between detection precision and detection time, a structure on the basis of adaptive compressive sampling for spectrum sensing is presented. Extensive simulation results are presented to justify the effectiveness of the proposed algorithms.