CNN-Enabled Multiple Power-Levels Identification in Cognitive Radio Networks-Reference-Cited by-同舟云学术

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CNN-Enabled Multiple Power-Levels Identification in Cognitive Radio Networks

Published:2022-12-04 Issue: Volume: Page:
ISSN:
Container-title:GLOBECOM 2022 - 2022 IEEE Global Communications Conference
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Author:

Tan Zhenyu¹,Liu Qi²,Li Zan¹,Wang Danyang¹,Zhang Ning³,Dai Hong-Ning⁴

Affiliation:

1. Xidian University,State Key Laboratory of Integrated Service Networks,Xi'an,China,710071

2. Xinjiang Astronomical Observatory, Chinese Academy of Sciences,Urumqi,830011

3. University of Windsor,Department of Electrical and Computer Engineering,Windsor,ON,Canada,N9B 3P4

4. Lingnan University,Hong Kong

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10000063/10000593/10001297.pdf?arnumber=10001297

Reference16 articles.

1. On the energy detection of unknown signals over fading channels

2. Deep Transfer Learning for Signal Detection in Ambient Backscatter Communications

3. Asymptotic theory of mixed time averages and kth-order cyclic-moment and cumulant statistics

4. Time series: Data analysis and theory, 2nd edition;brillinger;Society for Industrial and Applied Mathematics(SIAM) and Philadel-phia,1981

5. Deep CM-CNN for Spectrum Sensing in Cognitive Radio

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