基于压缩感知的非重构频谱感知算法研究

Non-reconstruction spectrum sensing algorithm based on compressive sensing

为了减小信号重构下频谱感知算法的计算复杂度和恢复频谱准确性对判决结果的影响,提出了基于压缩感知的非重构单节点频谱感知算法。在单节点频谱感知中,首先将信道划分成信道组,以降低采样协方差矩阵的计算复杂度;然后求得采样协方差矩阵对角线上的值并进行去噪,以提高检测性能。为了解决单节点频谱感知可能存在深度衰落和终端隐藏的问题,提出了多节点协作频谱感知并进行了系统仿真与分析。仿真结果表明,非重构单节点和多节点协作频谱感知算法都能够有效地检测出频谱空洞,并且多节点协作频谱感知检测的准确度更高。

A non-reconstruction spectrum sensing algorithm for single-node based on compressive sensing （CS） is proposed to reduce the computational complexity on the reconstruction and the influence of spectrum reconstruction accuracy on the decision result. In single-node spectrum sensing algorithm, the channels are firstly divided into channel groups to reduce the computational complexity of the sample covariance matrix. Then, the diagonal values of the sample covariance matrix are obtained, and detection performance is further improved by a de-noising algorithm. To solve the problems of the degraded detection performance, and deep fading channels and hidden terminals for single-node spectrum sensing, multi-node cooperative spectrum sensing is proposed, and the simulation and the analysis of the system are carried out. The simulation results show that the two non-reconstruction spectrum sensing algorithms for both single-node and cooperative multi-node can effectively detect the spectrum holes and the detection accuracy of multi-node cooperative spectrum sensing is higher.