ZTE Communications ›› 2022, Vol. 20 ›› Issue (3): 61-69.DOI: 10.12142/ZTECOM.202203008
• Research Paper • Previous Articles Next Articles
ZHANG Jintao1, HE Zhenqing1(), RUI Hua2,3, XU Xiaojing2,3
Received:
2022-03-06
Online:
2022-09-13
Published:
2022-09-14
About author:
ZHANG Jintao received his BS and MS degrees in communication engineering from the University of Electronic and Science Technology of China, in 2019 and 2022, respectively. His research interests include spectrum sensing and deep learning.|HE Zhenqing (Supported by:
ZHANG Jintao, HE Zhenqing, RUI Hua, XU Xiaojing. Spectrum Sensing for OFDMA Using Multicarrier Covariance Matrix Aware CNN[J]. ZTE Communications, 2022, 20(3): 61-69.
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URL: http://zte.magtechjournal.com/EN/10.12142/ZTECOM.202203008
Algorithms | Online Sensing | Offline Training |
---|---|---|
Energy detection[ | × | |
Eigenvalue-based methods[ | × | |
OMCM-CNN |
Table 1 Computational complexity of respective algorithms
Algorithms | Online Sensing | Offline Training |
---|---|---|
Energy detection[ | × | |
Eigenvalue-based methods[ | × | |
OMCM-CNN |
Input: Multicarrier Covariance Matrix Array ( | |
---|---|
Layers | Convolution Kernel Size |
C1+ ReLu | |
M1 | |
C2+ ReLu | |
M2 | |
C3+ ReLu | |
C4+ ReLu | |
M3 | |
F+ Sigmoid | |
Output: Feature Vector (方正汇总行 |
Table 2 Hyper parameters of the proposed CNN
Input: Multicarrier Covariance Matrix Array ( | |
---|---|
Layers | Convolution Kernel Size |
C1+ ReLu | |
M1 | |
C2+ ReLu | |
M2 | |
C3+ ReLu | |
C4+ ReLu | |
M3 | |
F+ Sigmoid | |
Output: Feature Vector (方正汇总行 |
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