中文摘要：

频谱感知是认知无线电技术的基础,随着通信技术不断发展,越来越高的采样速率成为一大瓶颈。实际应用中频谱占用通常具有稀疏性,根据这一特点并结合频谱检测要求,本文提出一种基于差分信号压缩感知（Differential Signal Compressed Sensing,DSCS）的宽带频谱感知方法。该方法在能量检测法的基础上引入压缩感知理论（compressed sensing,CS）,使系统能以远低于奈奎斯特采样速率的速率无损采样,降低对硬件的要求;为降低计算量、提高算法稳定性,采用检测差分信号代替检测信号本身作为判断频谱占用变更的依据;引入精度作为算法的迭代停止条件,可根据需要灵调整算法准确度、降低计算复杂度。仿真表明,适当精度下DSCS法能大幅降低迭代次数、减少计算量,并获得更好的检测性能。

英文摘要：

Spectrum sensing is a foundation in the cognitive radio technology.Normally,due to the hardware limitation,it＇s the high sampling rate,which is needed in signal sampling,especially in the case of wideband signals,that is more and more difficult to realize. Taking both the characteristics in the spectrum distribution and the request for spectrum sensing into consideration,this paper proposes a new spectrum sensing algorithm for the wideband cognitive radio based on Differential Signal Compressed Sensing（DSCS ） by the use of the sparsity of spectrum.Based on energy detection technology,the theory of compressed sensing（CS） is used to acquire signals at reduced rates,rather than the classical Shannon-Nyquist rate.To reduce the complexity,as well as to improve the sensing performance, differential signals,instead of normal ones,are taken to acquire spectrum informations.What＇s more,a new tolerance limitation is proposed to halt the iterations for a further reducation in complexity.This new limitation makes the algorithm much easier to match the practical requests-both in time and in computation.Related experiments demonstrate that with a proper toleranceε,the wide spectrum sensing methods for Wideband Cognitive Radio based on DSCS leads to a great reduce in both iteration and computation,in addition to a better sensing performance.