存在噪声不确定度的高斯白噪声下基于特征值频谱感知算法

doi:10.12305/j.issn.1001-506X.2025.06.32

摘要/Abstract

摘要：

针对传统频谱感知算法易受噪声不确定度影响, 提出一种基于采样协方差矩阵的最大特征值与信号能量之差(difference between maximum eigenvalue and signal energy, DMEE)的频谱感知算法。在存在噪声不确定度的高斯白噪声的环境下, 通过理论分析与仿真实验, 验证所提算法不存在信噪比墙的现象, 且不易受噪声不确定度的影响, 与基于次级用户接收端采样信号协方差矩阵的特征值的同类算法相比, 提升了存在噪声不确定度的高斯白噪声环境下的频谱感知性能。

关键词: 认知无线电, 频谱感知, 采样协方差, 噪声不确定度, 特征值

Abstract:

Since traditional spectrum sensing algorithms are susceptible to noise uncertainty, a spectrum sensing algorithm based on the difference between maximum eigenvalue and signal energy (DMEE) of the sampling covariance matrix is proposed. In the context of white Gaussian noise with noise uncertainty, theoretical analysis and simulation experiments verify that the proposed algorithm does not have the phenomenon of signal to noise ratio wall and is not easily affected by noise uncertainty. Compared with similar eigenvalue algorithms based on covariance matrix of sampling signal at the receiving end of secondary users, the spectrum sensing performance in the environment of white Gaussian noise with noise uncertainty is improved.

Key words: cognitive radio, spectrum sensing, sampling covariance, noise uncertainty, eigenvalue

中图分类号:

TN92

张睿, 陈增茂, 杨永琦. 存在噪声不确定度的高斯白噪声下基于特征值频谱感知算法[J]. 系统工程与电子技术, 2025, 47(6): 2047-2054.

Rui ZHANG, Zengmao CHEN, Yongqi YANG. Eigenvalue spectrum sensing algorithm for Gaussian white noise with noise uncertainty[J]. Systems Engineering and Electronics, 2025, 47(6): 2047-2054.

图/表 6

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参考文献 31

1	AL-DULAIMI O M K, AL-DULAIMI M K H, AL-DULAIMI A M K. Cognitive radio technologies and applications in dynamic spectrum access method[C]// Proc. of the IEEE 9th International Conference on Problems of Infocommunications, Science and Technology, 2022.
2	AL-SAGGAF U M , AHMAD J , ALREFAEI M A , et al. Optimized statistical beamforming for cooperative spectrum sensing in cognitive radio networks[J]. Mathematics, 2023, 11 (16): 3533. doi: 10.3390/math11163533
3	LI Y B , OUYANG W J , MIAO J W , et al. Joint optimization of sampling point and sensing threshold for spectrum sensing[J]. IET Communications, 2024, 18 (3): 235- 247. doi: 10.1049/cmu2.12730
4	FAN J C , BAN Y , LUO J , et al. Spectrum sensing based on angular reciprocity in cognitive satellite communication system[J]. Science China Information Sciences, 2021, 64 (8): 117- 126.
5	URKOWITZ H . Energy detection of unknown deterministic signals[J]. Proceedings of the IEEE, 1967, 55 (4): 523- 531. doi: 10.1109/PROC.1967.5573
6	TANDRA R, SAHAI A. Fundamental limits on detection in low SNR under noise uncertainty[C]//Proc. of the International Conference on Wireless Networks, Communications and Mobile Computing, 2005.
7	TANDRA R , SAHAI A . SNR walls for signal detection[J]. IEEE Journal of Selected Topics in Signal Processing, 2008, 2 (1): 4- 17. doi: 10.1109/JSTSP.2007.914879
8	LORINCZ J , RAMLJAK I , BEGUSIC D . Performance analysis of energy detection based on square-law combining in MIMO-OFDM cognitive radio networks[J]. Sensors, 2021, 21 (22): 7678- 7682. doi: 10.3390/s21227678
9	MURTI B B, HIDAYAT R, WIBOWO S B. Spectrum sensing using adaptive threshold based energy detection in cognitive radio system[C]//Proc. of the 4th International Seminar on Research of Information Technology and Intelligent Systems, 2021.
10	URIARTE I , MARTINEZ D M , ANDRADE A G , et al. Spectral minima tracking for improving the energy detection under noise power uncertainty[J]. Alexandria Engineering Journal, 2023, 66 (5): 619- 632.
11	MUZAFFAR M U , SHARQI R . A review of spectrum sensing in modern cognitive radio networks[J]. Telecommunication Systems, 2024, 85 (2): 347- 363. doi: 10.1007/s11235-023-01079-1
12	GARDNER W A . The spectral correlation theory of cyclosta tionary time-series[J]. Signal Processing, 1986, 11 (1): 13- 36. doi: 10.1016/0165-1684(86)90092-7
13	GARDNER W A . Measurement of spectral correlation[J]. IEEE Trans. on Acoustics, Speech, and Signal Processing, 1986, 34 (5): 1111- 1123. doi: 10.1109/TASSP.1986.1164951
14	SAGAR S A, PRATHIMA H, PREETHU J, et al. Cyclo-stationary spectrum sensing using GNU radio[C]//Proc. of the 2nd Global Conference for Advancement in Technology, 2021.
15	NOURI M , BEHROOZI H , MALLAT N K , et al. A wideband 5G cyclostationary spectrum sensing method by Kernel least mean square algorithm for cognitive radio networks[J]. IEEE Trans. on Circuits and Systems Ⅱ: Express Briefs, 2021, 68 (7): 2700- 2704. doi: 10.1109/TCSII.2021.3051087
16	SILVERSTEIN J W , COMBETTES P L . Signal detection via spectral theory of large dimensional random matrices[J]. IEEE Trans. on Signal Processing, 1992, 40 (8): 2100- 2105. doi: 10.1109/78.149981
17	ZHANG J L , LIU M Q , CHEN Y F , et al. Multi-antenna spectrum sensing with Alpha-stable noise for cognitive radio-enabled IoT[J]. IEEE Internet of Things Journal, 2024, 11 (12): 21546- 21558. doi: 10.1109/JIOT.2024.3374574
18	MEHRABIAN A , SABBAGHIAN M , YANIKOMEROGLU H . Spectrum sensing for symmetric α-stable noise model with con-volutional neural networks[J]. IEEE Trans. on Communications, 2021, 69 (8): 5121- 5135. doi: 10.1109/TCOMM.2021.3070892
19	ZENG Y , LIANG Y C . Eigenvalue-based spectrum sensing algorithms for cognitive radio[J]. IEEE Trans. on Communications, 2009, 57 (6): 1784- 1793. doi: 10.1109/TCOMM.2009.06.070402
20	CHARAN C , PANDEY R . Eigenvalue-based spectrum sensing under correlated noise for multi-dimensional cognitive radio receiver[J]. Wireless Personal Communications, 2023, 133 (1): 227- 244. doi: 10.1007/s11277-023-10765-x
21	ALI S S, ZHAO W, JIN M, et al. Enhanced maximum-minimum eigenvalue based spectrum sensing[C]//Proc. of the International Conference on Information and Communication Technology Convergence, 2019.
22	SURE P . On the performance of Grassmann-covariance-matrix-based spectrum sensing for cognitive radio[J]. Sādhanā, 2021, 46 (4): 222. doi: 10.1007/s12046-021-01719-9
23	SUN R, ZHANG J, KANG J. An covariance spectrum sensing algorithm based on DMM/MME mixture features[C]//Proc. of the 8th Annual International Conference on Network and Information Systems for Computers, 2022.
24	CHEN Z M, WANG H Y, SUN Z G, et al. Two novel spectrum sensing algorithms based on eigenvalue under different noise[C]// Proc. of the 15th IEEE International Conference on Signal Processing, 2020.
25	WANG P , FANG J , HAN N , et al. Multi-antenna-assisted spectrum sensing for cognitive radio[J]. IEEE Trans. on Vehicular Technology, 2009, 59 (4): 1791- 1800.
26	曹开田, 杨震. 基于随机矩阵理论的DET合作频谱感知算法[J]. 电子与信息学报, 2010, 32 (1): 129- 134.
	CAO K T , YANG Z . DET cooperative spectrum sensing algorithm based on random matrix theory[J]. Journal of Electronics and Information Technology, 2010, 32 (1): 129- 134.
27	TULINO A M , VERDU S . Random matrix theory and wireless communications[J]. Communications and Information Theory, 2004, 1 (1): 1- 182. doi: 10.1561/0100000001
28	TRACY C A , WIDOM H . On orthogonal and symplectic matrix ensembles[J]. Communications in Mathematical Physics, 1996, 177 (2): 727- 754.
29	弥寅, 卢光跃. 基于特征值极限分布的合作频谱感知算法[J]. 通信学报, 2015, 36 (1): 88- 93.
	MI Y , LU G Y . Cooperative spectrum sensing algorithm based on limiting eigenvalue distribution[J]. Journal on Communications, 2015, 36 (1): 88- 93.
30	曹开田, 杨震. 基于最小特征值的合作频谱感知新算法[J]. 仪器仪表学报, 2011, 32 (4): 736- 741.
	CAO K T , YANG Z . Novel cooperative spectrum sensing algorithm based on the smallest eigenvalue[J]. Chinese Journal of Scientific Instrument, 2011, 32 (4): 736- 741.
31	BAIK J , SILVERSTEIN J W . Eigenvalues of large sample covariance matrices of spiked population models[J]. Journal of Multivariate Analysis, 2006, 97 (6): 1382- 1408.

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