Performance Analysis of Convolutional Neural Networks with Different Window Functions for Automatic Modulation Classification

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F25547526%3A_____%2F22%3AN0000002" target="_blank" >RIV/25547526:_____/22:N0000002 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9952750" target="_blank" >https://ieeexplore.ieee.org/document/9952750</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICTC55196.2022.9952750" target="_blank" >10.1109/ICTC55196.2022.9952750</a>

Result language
angličtina
Original language name
Performance Analysis of Convolutional Neural Networks with Different Window Functions for Automatic Modulation Classification
Original language description
Automatic modulation classification is one of the main tasks for developing information security and radio signal surveillance systems. It is also becoming increasingly significant for spectrum monitoring, management, and secure communications. Recently, deep learning models have been widely applied in many fields due to their outstanding feature extraction and classification accuracy. In this paper, the automatic modulation classification performance of several deep convolutional neural networks (CNN) is analyzed on the Fast Fourier Transform (FFT) based signal spectrum and the Short Time Fourier Transform (STFT) based signal spectrogram. By using ResNet18 and MobileNet cross-combined with FFT and STFT input data, the simulation shows that the STFT data provides a higher AMC accuracy than that of FFT data. On the same STFT data, the ResNet18 model outperforms three other models (SqueezeNet, GoogleNet, and MobileNet) in classifying 26 modulation types under the influence of five levels of fading noise with SNRs from −20 dB to +18 dB. Besides, the impact of different window functions in STFT is also investigated. Numerical results indicate that the considered window functions cause an insignificant difference in the AMC accuracy.
Czech name
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Czech description
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Project
<a href="/en/project/TM02000035" target="_blank" >TM02000035: NEO classification of signals (NEOCLASSIG) for radio surveillance systems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2022
Confidentiality
C - Předmět řešení projektu podléhá obchodnímu tajemství (§ 504 Občanského zákoníku), ale název projektu, cíle projektu a u ukončeného nebo zastaveného projektu zhodnocení výsledku řešení projektu (údaje P03, P04, P15, P19, P29, PN8) dodané do CEP, jsou upraveny tak, aby byly zveřejnitelné.

Article name in the collection
Performance Analysis of Convolutional Neural Networks with Different Window Functions for Automatic Modulation Classification
ISBN
978-1-6654-9940-8
ISSN
2162-1233
e-ISSN
2162-1241
Number of pages
5
Pages from-to
153 - 157
Publisher name
IEEE
Place of publication
Jeju Island, Republic of Korea
Event location
Jeju Island, Republic of Korea
Event date
Jan 1, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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