基于特征值高阶矩的频谱感知增强技术

doi:10.12305/j.issn.1001-506X.2022.10.30

摘要/Abstract

摘要：

基于特征值的多天线盲频谱感知方法在认知无线电中得到了广泛的研究。基于特征值的检测算法设计依赖于随机矩阵理论和样本协方差矩阵的特征值特性。许多研究表明特征值的高阶矩可以提供额外的鉴别信息进而能改善统计推断问题的性能。基于此, 利用所有特征值的p阶矩提出了新的基于特征值高阶矩的频谱感知增强算法, 并利用随机矩阵理论推导了虚警概率和判决门限的解析表示。此外, 基于仿真实验研究了特征值高阶矩幂次变化对检测性能的影响。最后, 通过仿真实验验证了所提算法的有效性。

关键词: 认知无线电, 频谱感知, 特征值的高阶矩

Abstract:

Blind spectrum sensing algorithms based on eigenvalues has been widely studied in multi-antenna cognitive radio networks. The design of detection algorithms based on eigenvalues relies on the random matrix theory and the characteristics of eigenvalues of sample covariance matrix. Some work has shown that high order moments of eigenvalues can provide additional identification information to improve the performance of statistical inference problems. Motivated by this, an improved spectrum sensing algorithm resorting to the pth moment of all the eigenvalues is proposed, and the analytical representations of false alarm probability and decision threshold are given using the random matrix theory. Moreover, the influence of the power change of high-order moments on the detection performance is analyzed through simulation experiments. The simulation results show the effectiveness of the proposed spectrum sensing algorithm.

Key words: cognitive radio, spectrum sensing, high order moments of eigenvalues

中图分类号:

TN925

李贺, 赵文静, 金明录. 基于特征值高阶矩的频谱感知增强技术[J]. 系统工程与电子技术, 2022, 44(10): 3243-3248.

He LI, wenjing ZHAO, Minglu JIN. Improved spectrum sensing algorithms based on high order moments of eigenvalues[J]. Systems Engineering and Electronics, 2022, 44(10): 3243-3248.

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