| 摘要: |
| 针对协方差特征值算法构造检测统计量时对协方差矩阵信息利用不够充分导致低信噪比下检测性能衰减问题，提出一种特征值之差与调和平均之比频谱感知算法。该算法以协方差矩阵的最大最小特征值与特征值的调和平均构造检测统计量，更全面地利用协方差矩阵中的特征值信息，以提升算法检测性能。同时，该算法基于随机矩阵的特征值极限分布理论引入一种新的调和平均求解方式，旨在提高判决门限精确性的同时进一步提升检测性能。仿真实验表明，改进算法无需主用户及信道的先验信息，在信噪比为-20 dB时，其检测概率较其他几种经典算法有不低于10%的提升。 |
| 关键词: 认知无线电 频谱感知 随机矩阵 |
| DOI：10.20079/j.issn.1001-893x.240722005 |
|
| 基金项目:陕西省科技计划项目（2020GY-029 |
|
| Blind Spectrum Sensing Based on Random Matrix |
| YIN Xiaohu,TIAN Chong,ZHANG Keke,ZHANG Anyi |
| (College of Communication and Information Engineering,Xian University of Science and Technology,Xian 710600,China) |
| Abstract: |
| For the issue of decreased detection performance under low signal-to-noise ratio(SNR conditions due to insufficient utilization of covariance matrix information in covariance-based eigenvalue algorithms for constructing detection statistics,a novel spectral sensing algorithm based on the ratio of the difference between the maximum and minimum eigenvalues to the harmonic mean of eigenvalues is proposed.This algorithm constructs the detection statistic by incorporating both the extreme eigenvalues and the harmonic mean of eigenvalues from the covariance matrix,thereby more comprehensively exploiting the eigenvalue information within the covariance matrix to enhance the detection capability.Furthermore,a novel approach for calculating the harmonic mean is introduced,leveraging the asymptotic distribution theory of eigenvalues in random matrices.This approach aims to not only improve the accuracy of the decision threshold but also further boost the detection performance.Simulation results demonstrate that the proposed algorithm, |
| Key words: cognitive radio spectrum sensing random matrix |
