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Comparison of CNN Architectures using RP Algorithm for Burst Signal Detection

Title
Comparison of CNN Architectures using RP Algorithm for Burst Signal Detection
Author
남해운
Keywords
Cognitive radio; deep learning; convolutional neural network; burst signal detection; recurrence plot
Issue Date
2020-10
Publisher
KICS
Citation
2020 International Conference on Information and Communication Technology Convergence (ICTC), page. 809-812
Abstract
Recently, convolutional neural networks (CNNs) achieved remarkable success in various fields, especially computer vision and image processing. However, it is not known what type of CNN architecture is the best fit for the detection or classification of communication signals. In this work, we compare the three of CNN architecture in a burst signal detection task. The three CNN architectures are compared to their detection performance and computational complexity. The 9-layer CNN is shown to achieve a similar performance of 12-layer CNN on overall environments. The performance of the 7-layer CNN model is worse than that of the other two types of CNN architectures, except in terms of the computational complexity at low SNR.
URI
https://ieeexplore.ieee.org/document/9289320?arnumber=9289320&SID=EBSCO:edseeehttps://repository.hanyang.ac.kr/handle/20.500.11754/165331
ISBN
978-1-7281-6758-9
ISSN
2162-1233
DOI
10.1109/ICTC49870.2020.9289320
Appears in Collections:
COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > ELECTRICAL ENGINEERING(전자공학부) > Articles
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