基于极限特征值之差的改进频谱感知算法

Improved Spectrum Sensing Algorithm Based on the Difference of Limit Eigenvalues

基于取样协方差矩阵的最大和最小特征值之差的频谱感知算法,从提高感知判决门限的设置准确度出发,利用Wishart随机矩阵极限特征值分布的最新成果,设计出了一种新的判决门限计算方法.仿真实验结果表明,相对于现有的频谱感知算法,新算法的判决门限计算更加准确,在改善判决结果可靠性的同时,有效地提升了有限样本数量条件下算法的检测性能.

Based on the difference between the maximum and minimum eigenvalues(DMME)of the sample covariance matrix,to improve the setting accuracy of sensing decision threshold,a new method is proposed to calculate the threshold by using the latest results of limiting eigenvalue distribution of Wishart matrix.Compared with the traditional DMME algorithm,the decision threshold setting of the proposed algorithm is more accurate.While improving the reliability of decision results,it improves the detection performance of the algorithm with limited number of samples.Both the theoretical analysis and simulation results verify the effectiveness of the proposed DMME algorithm.