采用压缩采样匹配追踪算法的频谱感知

频谱资源的匮乏成为无线电发展的瓶颈，解决频率匮乏有两类方法:一是提高频谱利用率；二是扩大可利用的频率范围。频谱感知技术能提高频谱利用率，但在高频的应用中会面对过高的采样速率、较大的数据量，这对硬件实现提出了艰巨的挑战。本文根据无线电频谱稀疏性介绍一种基于调制宽带转换器的压缩采样匹配追踪（CoSaMP）算法。本文利用调制宽带转换器对无线电信号进行亚奈奎斯特采样，再利用CoSaMP算法对采样后的自相关矩阵求解。本文的方法不仅能应用在更高的频谱，且能提高频谱利用率。仿真结果表明:该方法能在能以一个较低的采样速率对信号进行采样，且CoSaMP算法的恢复误差要小于OMP和ROMP算法。 

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.