基于空间谱的频谱感知算法及性能分析

Spatial Spectrum Based Spectrum Sensing Algorithm and Performance Analysis

现有的基于特征值或谱密度的频谱感知算法,多分别使用近似高斯分布和Tracy-Widom分布来分别分析求解检验统计量在信号是否存在时的分布,未能给出统一的解析表达式。该文提出均匀线阵(ULA)条件下基于空间谱密度比的频谱感知算法,并且基于顺序统计量的最新研究成果,给出检验统计量统一的闭合表达式。该算法基于离散空间谱密度最大最小值的比建立检验统计量。仿真结果表明,对于8阵元的ULA,在采样点数为1000、检测概率为0.9时,所提算法比最大最小特征值(MME)比算法有约1.7 d B的性能优势,同时也有效验证了检验统计量理论分布的准确性。

Spectrum sensing algorithms based on eigenvalue or spectral density usually use the Gaussian approximated distribution and Tracy-Widom distribution to analyze the test statistic with the presence of the primary user or not respectively, but it is hard to find the analysis expression with unified form. In this paper, a spectrum sensing algorithm is proposed based on spatial spectrum density ratio using a Uniform Linear Array（ULA）, and a unified expression for the distribution of test statistic is proposed using the latest research results of order statistics. In this algorithm, the test statistic is established using the maximum and minimum values of the discrete spatial spectrum density. Simulation results show that the performance of the proposed algorithm is about 1.7 d B better than the Maximum-Minimum Eigenvalue（MME） ratio algorithm with the detection probability equal to 0.9. At the same time, the results also verify the accuracy of the theoretical distribution of the test statistic.