TY - GEN
T1 - Signal detection and modulation recognition based on convolutional neural networks
AU - Luo, Yuguang
AU - Xu, Mingdong
AU - Wu, Zhilu
AU - Yin, Zhendong
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In modern electronic countermeasures systems, accurate detection of unknown user signals and identification of signal modulation mode are essential for signal demodulation and communication countermeasures. Energy detector is a traditional unknown signal detection method, but it has SNR-wall problem, and it cannot distinguish different types of signals. In this paper, to address the problems encountered by traditional methods, we first propose a signal detector based on neural networks. It uses the time domain waveform information of the signal to achieve good detection performance, and it can work without relevant prior knowledge. Furthermore, the effects of different modulation modes and SNR on performance are studied. Finally, a signal classifier based on deep learning is proposed, which can achieve good classification performance for detected signals.
AB - In modern electronic countermeasures systems, accurate detection of unknown user signals and identification of signal modulation mode are essential for signal demodulation and communication countermeasures. Energy detector is a traditional unknown signal detection method, but it has SNR-wall problem, and it cannot distinguish different types of signals. In this paper, to address the problems encountered by traditional methods, we first propose a signal detector based on neural networks. It uses the time domain waveform information of the signal to achieve good detection performance, and it can work without relevant prior knowledge. Furthermore, the effects of different modulation modes and SNR on performance are studied. Finally, a signal classifier based on deep learning is proposed, which can achieve good classification performance for detected signals.
KW - Artificial neural networks
KW - Modulation classification
KW - Signal detection
KW - Spectrum sensing
UR - https://www.scopus.com/pages/publications/85136666042
U2 - 10.1109/ICAICA54878.2022.9844646
DO - 10.1109/ICAICA54878.2022.9844646
M3 - 会议稿件
AN - SCOPUS:85136666042
T3 - 2022 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2022
SP - 295
EP - 299
BT - 2022 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2022
Y2 - 24 June 2022 through 26 June 2022
ER -