Eigenvalue difference spectrum sensing algorithm under Alpha stable distributed noise

被引：0

作者：

Chen Z. ^{[1
,2
]}

Wang K. ^{[1
]}

Sun Z. ^{[1
]}

Sun R. ^{[1
]}

Aer S. ^{[1
]}

机构：

[1] School of Information and Communication Engineering, Harbin Engineering University, Harbin

[2] Key Laboratory of Advanced Marine Communication and Information Technology, Harbin Engineering University, Ministry of Industry and Information Technology, Harbin

来源：

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2023年 / 45卷 / 09期

关键词：

Alpha stable distributed; fractional low order moments; geometric mean; sampling covariance; spectrum sensing;

D O I：

10.12305/j.issn.1001-506X.2023.09.35

中图分类号：

学科分类号：

摘要：

Aiming at the problem that the spectral sensing algorithm based on eigenvalue has poor sensing performance in the environment of impulse noise. All eigenvalues of the matrix are analyzed and the geometric mean of the eigenvalues of the matrix is introduced. A spectrum sensing algorithm based on the difference between maximum eigenvalue and geometric mean of eigenvalue (DMGM) of the fractional low-order covariance matrix is proposed. Alpha stable distribution noise is selected to simulate the impulse noise environment. Theoretical analysis and simulation results show that DMGM has better perceptual performance than other algorithms in low signal to noise ratio environments, and has better perceptual performance under low signal to noise ratio conditions. © 2023 Chinese Institute of Electronics. All rights reserved.

引用

页码：2949 / 2955

页数：6

共 30 条

[1] DUAN Y S, ZHANG M B, AN Y W., Optimization of cooperative spectrum sensing for OFDM in multiuser scenarios, Proc. of the 8th Conference on Joint International Information Technology and Artificial Intelligence, pp. 1596-1600, (2019)
[2] ZHUANG J W, ZHANG Y H, ZHANG S C, Et al., A multi-antenna spectrum sensing scheme based on main information extraction and genetic algorithm clustering, IEEE Access, 7, pp. 119620-119630, (2019)
[3] ZHANG Z S, LONG K P, WANG J P., Self-organization paradigms and optimization approaches for cognitive radio technologies: a survey, IEEE Wireless Communications, 20, 2, pp. 36-42, (2013)
[4] CHEN S J, WANG X, SHEN B., A support vector machine based spectrum sensing for cognitive radios, Journal of Chongqing University of Posts and Telecommunication (Natural Science Edition), 31, 3, pp. 321-322, (2019)
[5] AMRUTHA V, KARTHIKEYAN K V., Spectrum sensing methodologies in cognitive radio networks
[6] a survey, Proc. of the Conference on Innovations in Electrical, Instrumentation and Media Technology, pp. 306-310, (2017)
[7] AWIN F, ABDEDL-RAIIEEM E, TEPE K., Blind spectrum sensing approaches for interweaved cognitive radio system
[8] a tutorial and short course, IEEE Communications Surveys and Tutorials, 21, 1, pp. 238-259, (2019)
[9] MALLAT S, YU G S., Super-resolution with sparse mixing esti-mators, IEEE Trans, on Image Processing, 19, 11, pp. 2889-2900, (2010)
[10] FURUTSU K, ISIIIDA T., On the theory of amplitude distribution of impulsive random noise, Journal of Applied Physics, 32, 7, pp. 1206-1221, (1961)

← 1 2 3 →