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

Abstract

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

CLC Number:

TN925

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.

Figures/Tables 7

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References 31

1	HAYKIN S , SETOODEH P . Cognitive radio networks: the spectrum supply chain paradigm[J]. IEEE Trans.on Cognitive Communications and Networking, 2015, 1 (1): 3- 28. doi: 10.1109/TCCN.2015.2488627
2	KOLODET P J. Spectrum policy task force report[R]. Wash-ington, D.C. : Federal Communications Commission, 2002.
3	DATLA D , WYGLINSKI M , MINDEN J . A spectrum surveying framework for dynamic spectrum access networks[J]. IEEE Trans.on Vehicular Technology, 2009, 58 (8): 4158- 4168. doi: 10.1109/TVT.2009.2021601
4	LIANG Y C , CHEN K C , LI Y , et al. Cognitive radio networking and communications: an overview[J]. IEEE Trans.on Vehicular Technology, 2011, 60 (7): 3386- 3407. doi: 10.1109/TVT.2011.2158673
5	KANG S , JOO C . Low-complexity learning for dynamic spectrum access in multi-user multi-channel networks[J]. IEEE Trans.on Mobile Computing, 2021, 22 (11): 3267- 3281.
6	SHBAT M, ORDAZ-SALAZAR F C, GONZÁLEZ-SALAS J S. Spectrum sensing challenges of IOT nodes designed under 5G network standards[C]//Proc. of the IEEE 15th International Conference on Electrical Engineering, Computing Science and Automatic Control, 2018.
7	EJAZ W , IBNKAHLA M . Multiband spectrum sensing and resource allocation for IoT in cognitive 5G networks[J]. IEEE Internet of Things Journal, 2018, 5 (1): 150- 163. doi: 10.1109/JIOT.2017.2775959
8	KATZ M, MATINMIKKO-BLUE M, LATVA-AHO M. 6 Genesis flagship program: building the bridges towards 6G-enabled wireless smart society and ecosystem[C]//Proc. of the IEEE 10th Latin-American Conference on Communications, 2018.
9	KASSA Z , KHALIFE J , NEINAVAIE N . Cognitive opportunistic navigation in private networks with 5G signals and beyond[J]. IEEE Journal of Selected Topics in Signal Processing, 2021, 16 (1): 129- 143.
10	ALI A , HAMOUDA W . Advances on spectrum sensing for cognitive radio networks: theory and applications[J]. IEEE Communications Surveys & Tutorials, 2017, 19 (2): 1277- 1304.
11	AWIN F , ABDEL-RAHEEM E , TEPE K . Blind spectrum sensing approaches for interweaved cognitive radio system: a tutorial and short course[J]. IEEE Communications Surveys & Tutorials, 2019, 21 (1): 238- 259.
12	QIN Z , ZHOU X , ZHANG L , et al. 20 years of evolution from cognitive to intelligent communications[J]. IEEE Trans.on Cognitive Communications and Networking, 2020, 6 (1): 6- 20. doi: 10.1109/TCCN.2019.2949279
13	MYKE D, CELSO B, WALDIR S. Trends and challenges for the spectrum sensing in the next generation of communication systems[C]//Proc. of the IEEE International Conference on Consumer Electronics, 2020.
14	冉洁, 司宾强. 认知无线电频谱感知技术综述[C]//第十四届全国信号和智能信息处理与应用学术会议, 2021.
	RAN J, SI B Q. Overview of spectrum sensing technology in cognitive radio techniques[C]//Proc. of the 14th National Conference on Signal and Intelligent Information Processing and Application, 2021.
15	余盼, 李斌, 赵成林. 基于能量检测的异步感知算法[J]. 通信学报, 2017, 38 (3): 165- 173.
	YU P , LI B , ZHAO C L . Asynchronous perception algorithm based on energy detection[J]. Journal on Communications, 2017, 38 (3): 165- 173.
16	许炜阳, 李有均, 徐宏乾, 等. 基于随机矩阵非渐近谱理论的协作频谱感知算法研究[J]. 电子与信息学报, 2018, 40 (1): 123- 129.
	XU W Y , LI Y J , XU H Q , et al. Study on cooperative spectrum sensing algorithm based on random matrix non-asymptotic spectral theory[J]. Journal of Electronics & Information Technology, 2018, 40 (1): 123- 129.
17	KAY S M. Fundamentals of statistical signal processing, volume Ⅱ: detection theory[M]. Upper Saddle River: Prentice Hall, 1998.
18	DIGHAM F F , ALOUINI M S , SIMONM K . On the energy detection of unknown signals over fading channels[J]. IEEE Trans.on Communications, 2007, 55 (1): 21- 24. doi: 10.1109/TCOMM.2006.887483
19	URKOWITZ H . Energy detection of unknown deterministic signals[J]. Proceedings of IEEE, 1967, 55 (4): 523- 531.
20	GISMALLA H , ALSUSA E . Performance analysis of the perio-dogram-based energy detector in fading channels[J]. IEEE Trans.on Signal Processing, 2011, 59 (8): 3712- 3721.
21	YUCEK T , ARSLAN H . A survey of spectrum sensing algorithms for cognitive radio applications[J]. IEEE Communications Surveys & Tutorials, 2009, 11 (1): 116- 130.
22	LIU C , LI H S , WANG J , et al. Optimal eigenvalue weighting detection for multi-antenna cognitive radio networks[J]. IEEE Trans.on Wireless Communications, 2017, 16 (4): 2083- 2096.
23	ZENG Y , LIANG Y C . Eigenvalue-based spectrum sensing algorithms for cognitive radio[J]. IEEE Trans.on Communications, 2009, 57 (6): 1784- 1793.
24	TAHERPOUR A , NASIRI-KENARI M , GAZOR S . Multiple antenna spectrum sensing in cognitive radios[J]. IEEE Trans.on Wireless Communications, 2010, 9 (2): 814- 823.
25	WANG P , FANG J , HAN N , et al. Multiantenna-assisted spectrum sensing for cognitive radio[J]. IEEE Trans.on Vehicular Technology, 2010, 59 (4): 1791- 1800.
26	PILLAY N , XU H J . Blind eigenvalue-based spectrum sensing for cognitive radio networks[J]. IET Communications, 2012, 6 (11): 1388- 1396.
27	JOHNSTONE I M . On the distribution of the largest eigenvalue in principal components analysis[J]. The Annals of Statistics, 2001, 29 (2): 295- 327.
28	YAZDIAN E , GAZOR S , BASTANI H . Source enumeration in large arrays using moments of eigenvalues and relatively few samples[J]. IET Signal Processing, 2012, 6 (7): 689- 696.
29	SEDIGHI S , TAHERPOUR A , GAZOR S , et al. Eigenvalue-based multiple antenna spectrum sensing: higher order moments[J]. IEEE Trans.on Wireless Communications, 2017, 16 (2): 1168- 1184.
30	HUANG L , FANG J , LIU K F , et al. An eigenvalue-moment-ratio approach to blind spectrum sensing for cognitive radio under sample-starving environment[J]. IEEE Trans.on Vehicular Technology, 2015, 64 (8): 3465- 3480.
31	赵文静, 李贺, 金明录. 基于特征值的频谱感知融合算法[J]. 通信学报, 2019, 40 (11): 57- 64.
	ZHAO W J , LI H , JIN M L . Fusion spectrum sensing algorithm based on eigenvalues[J]. Journal on Communications, 2019, 40 (11): 57- 64.

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Improved spectrum sensing algorithms based on high order moments of eigenvalues

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