基于采样协方差矩阵的频谱感知判决门限优化

Spectrum Sensing Decision Threshold Optimization Based on Sampling Covariance Matrix

在认知无线电频谱感知算法中,传统的基于采样协方差矩阵特征值极限分布函数的频谱感知算法难以同时实现高检测概率和低虚警概率.在此基础上,提出了一种基于采样协方差矩阵的频谱感知判决门限优化方法.利用随机矩阵理论,分别得到了采样协方差矩阵最大特征值和最小特征值极限分布函数下的判决门限,并将两个判决门限的加权求和作为最终判决门限.仿真结果表明,在获得较高检测概率的情况下,优化判决门限仍可保持较低的虚警概率.

In cognitive radio spectrum sensing algorithm,the traditional spectrum sensing algorithm based on sampling covariance matrix eigenvalue limit distribution function is difficult to achieve both high detection probability and low false alarm probability.Based on this,a decision threshold optimization method for spectrum sensing based on sampling covariance matrix is proposed.By using the random matrix theory,the decision thresholds under the limit distribution function of the maximum and minimum eigenvalues of the sampling covariance matrix are obtained respectively,and the weighted summation of the two decision thresholds is used as the final decision threshold.The simulation results show that the optimized decision threshold can still maintain a low false alarm probability under the condition of obtaining a higher detection probability.