Spectrum Sensing using the algorithm of Compressive Sampling Matching Pursuit

The lack of radio spectrum resources becomes the bottleneck in radio. There are two ways to solve the shortage of frequencies: the first is to improve the spectrum utilization; the second is to expand the available frequency range. The technology of spectrum sensing could improve the spectrum efficiency, but the application of high frequency faces a high sampling rate,a large amount of data, and it is a huge challenge for the hardware implementation. In this paper, the modulated wideband converter with compressive sampling matching pursuit (CoSaMP) algorithm based on the sparsity of the radio spectrum is used in spectrum sensing. The radio signal is sampled by using the modulated wideband converter with a sub-Nyquist sampling rate and then CoSaMP algorithm is used to solve the sampled autocorrelation matrix. The proposed method can not only used in the high spectrum, but also can improve spectrum utilization. The simulation results show that this method can sample the signal with a lower sampling rate, and the recovered errors of CoSaMP algorithm are less than OMP and ROMP algorithm.