Enhanced Maximum-Minimum Eigenvalue Based Spectrum Sensing

Syed Sajjad Ali; Wenjing Zhao; Minglu Jin; Sang Jo Yoo

doi:10.1109/ICTC46691.2019.8939923

Enhanced Maximum-Minimum Eigenvalue Based Spectrum Sensing

Syed Sajjad Ali, Wenjing Zhao, Minglu Jin, Sang Jo Yoo

Dalian University of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

The maximum eigenvalue captures the signal correlation well, and the minimum eigenvalue also captures the noise characteristics well, thus the spectrum sensing algorithm based on the maximum and minimum eigenvalue gets better detection performance. This paper considers various combination algorithms based on maximum and minimum eigenvalues, and proposes some new spectrum sensing algorithms based on maximum and minimum eigenvalue which includes well known algorithm as its special case. Simulation result for multi-user, multi-antenna and multi-path scenarios shows the effectiveness of the proposed algorithms. In particular, the algorithm based on the product and sum of the maximum and minimum eigenvalues (α-MMEP and α-MMES) showed the best detection performance.

Original language	English
Title of host publication	ICTC 2019 - 10th International Conference on ICT Convergence
Subtitle of host publication	ICT Convergence Leading the Autonomous Future
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	708-713
Number of pages	6
ISBN (Electronic)	9781728108926
DOIs	https://doi.org/10.1109/ICTC46691.2019.8939923
State	Published - Oct 2019
Event	10th International Conference on Information and Communication Technology Convergence, ICTC 2019 - Jeju Island, Korea, Republic of Duration: 16 Oct 2019 → 18 Oct 2019

Publication series

Name	ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future

Conference

Conference	10th International Conference on Information and Communication Technology Convergence, ICTC 2019
Country/Territory	Korea, Republic of
City	Jeju Island
Period	16/10/19 → 18/10/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

Cognitive radio network
maximum eigenvalue
minimum eigenvalue
spectrum sensing

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10.1109/ICTC46691.2019.8939923

Cite this

Ali, S. S., Zhao, W., Jin, M., & Yoo, S. J. (2019). Enhanced Maximum-Minimum Eigenvalue Based Spectrum Sensing. In ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future (pp. 708-713). Article 8939923 (ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICTC46691.2019.8939923

Ali, Syed Sajjad ; Zhao, Wenjing ; Jin, Minglu et al. / Enhanced Maximum-Minimum Eigenvalue Based Spectrum Sensing. ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 708-713 (ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future).

@inproceedings{40b10add66d9436aaaee47828714a2d5,

title = "Enhanced Maximum-Minimum Eigenvalue Based Spectrum Sensing",

abstract = "The maximum eigenvalue captures the signal correlation well, and the minimum eigenvalue also captures the noise characteristics well, thus the spectrum sensing algorithm based on the maximum and minimum eigenvalue gets better detection performance. This paper considers various combination algorithms based on maximum and minimum eigenvalues, and proposes some new spectrum sensing algorithms based on maximum and minimum eigenvalue which includes well known algorithm as its special case. Simulation result for multi-user, multi-antenna and multi-path scenarios shows the effectiveness of the proposed algorithms. In particular, the algorithm based on the product and sum of the maximum and minimum eigenvalues (α-MMEP and α-MMES) showed the best detection performance.",

keywords = "Cognitive radio network, maximum eigenvalue, minimum eigenvalue, spectrum sensing",

author = "Ali, {Syed Sajjad} and Wenjing Zhao and Minglu Jin and Yoo, {Sang Jo}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 10th International Conference on Information and Communication Technology Convergence, ICTC 2019 ; Conference date: 16-10-2019 Through 18-10-2019",

year = "2019",

month = oct,

doi = "10.1109/ICTC46691.2019.8939923",

language = "English",

series = "ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Ali, SS, Zhao, W, Jin, M & Yoo, SJ 2019, Enhanced Maximum-Minimum Eigenvalue Based Spectrum Sensing. in ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future., 8939923, ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future, Institute of Electrical and Electronics Engineers Inc., pp. 708-713, 10th International Conference on Information and Communication Technology Convergence, ICTC 2019, Jeju Island, Korea, Republic of, 16/10/19. https://doi.org/10.1109/ICTC46691.2019.8939923

Enhanced Maximum-Minimum Eigenvalue Based Spectrum Sensing. / Ali, Syed Sajjad; Zhao, Wenjing; Jin, Minglu et al.
ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future. Institute of Electrical and Electronics Engineers Inc., 2019. p. 708-713 8939923 (ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Enhanced Maximum-Minimum Eigenvalue Based Spectrum Sensing

AU - Ali, Syed Sajjad

AU - Zhao, Wenjing

AU - Jin, Minglu

AU - Yoo, Sang Jo

PY - 2019/10

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N2 - The maximum eigenvalue captures the signal correlation well, and the minimum eigenvalue also captures the noise characteristics well, thus the spectrum sensing algorithm based on the maximum and minimum eigenvalue gets better detection performance. This paper considers various combination algorithms based on maximum and minimum eigenvalues, and proposes some new spectrum sensing algorithms based on maximum and minimum eigenvalue which includes well known algorithm as its special case. Simulation result for multi-user, multi-antenna and multi-path scenarios shows the effectiveness of the proposed algorithms. In particular, the algorithm based on the product and sum of the maximum and minimum eigenvalues (α-MMEP and α-MMES) showed the best detection performance.

AB - The maximum eigenvalue captures the signal correlation well, and the minimum eigenvalue also captures the noise characteristics well, thus the spectrum sensing algorithm based on the maximum and minimum eigenvalue gets better detection performance. This paper considers various combination algorithms based on maximum and minimum eigenvalues, and proposes some new spectrum sensing algorithms based on maximum and minimum eigenvalue which includes well known algorithm as its special case. Simulation result for multi-user, multi-antenna and multi-path scenarios shows the effectiveness of the proposed algorithms. In particular, the algorithm based on the product and sum of the maximum and minimum eigenvalues (α-MMEP and α-MMES) showed the best detection performance.

KW - Cognitive radio network

KW - maximum eigenvalue

KW - minimum eigenvalue

KW - spectrum sensing

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M3 - Conference contribution

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PB - Institute of Electrical and Electronics Engineers Inc.

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Ali SS, Zhao W, Jin M, Yoo SJ. Enhanced Maximum-Minimum Eigenvalue Based Spectrum Sensing. In ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future. Institute of Electrical and Electronics Engineers Inc. 2019. p. 708-713. 8939923. (ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future). doi: 10.1109/ICTC46691.2019.8939923

Enhanced Maximum-Minimum Eigenvalue Based Spectrum Sensing

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