用于宽带频谱感知的全盲亚奈奎斯特采样方法

摘要

Sub-Nyquist sampling is an effective approach to mitigate the high sampling rate pressure for wideband spectrum sensing. The existing sub-Nyquist sampling method requires excessive large measurement matrix and exact sparsity level in recovery phase. Considering this problem, a method of applying Modulated Wideband Converter (MWC) with small measurement matrix to wideband spectrum sensing is proposed. An improved sufficient condition for spectrum-blind recovery based on the redefinition of spectrum sparse signal model is presented, which breaks the dependence on the maximum width of bands for MWC construction. In recovery phase, the Sparsity Adaptive Matching Pursuit (SAMP) algorithm is introduced to Multiple Measurement Vector (MMV) problem. As a result, a full-blind low rate sampling method requiring neither the maximum width nor the exact number of bands is implemented. The experimental results verify the effectiveness of the proposed method.%亚奈奎斯特采样方法是缓解宽带频谱感知技术中采样率过高压力的有效途径.该文针对现有亚奈奎斯特采样方法所需测量矩阵维数过大且重构阶段需要确切稀疏度的问题,提出了将测量矩阵较小的调制宽带转换器(MWC)应用于宽带频谱感知的方法.在重新定义频谱稀疏信号模型的基础上,提出了一个改进的盲谱重构充分条件,消除了构建MWC系统对最大频带宽度的依赖；在重构阶段,将稀疏度自适应匹配追踪(SAMP)算法引入到多测量向量(MMV)问题的求解中.最终实现了既不需要预知最大频带宽度也不需要确切频带数量的全盲低速采样,实验结果验证了该方法的有效性.