摘要： 针对认知用户接收的未知稀疏度信号，提出一种基于盲稀疏度匹配追踪的协同频谱检测算法。该算法自动调节候选集原子的数量后，在迭代过程中采用阶段转换得到稀疏度，并利用回退机制获得全局最优支撑集，同时通过SNR估计选择最优协作用户进行联合检测，从而实现频谱的快速检测。实验结果表明，在相同条件下，该算法的检测效果优于同类算法，检测率比无选择对象的协作检测方法提高 约25%。

关键词: 压缩感知, 认知无线电, 匹配追踪, 盲稀疏度, 协同频谱检测

Abstract: A collaborative spectrum detection based on backtracking blind sparsity matching pursuit algorithm is proposed for sparse signals with unknown sparsity which the cognitive radio users receive. The algorithm can control the rapidity and accuracy of spectrum detection. It chooses the candidate set automatically, adopts staged changing process to estimate sparsity and backoff mechanism to obtain the global optimal support sets, and chooses the optimal collaborative users although SNR estimate. Experimental results show that the algorithm is superior to other algorithms in the same test conditions, and the probability of detection is increased by about 25% than the collaborative detection method of nonselective object.

Key words: compressive sensing, cognitive radio, matching pursuit, blind sparsity, collaborative spectrum detection

中图分类号: 

• TN929