TY - GEN
T1 - A Modulation Recognition Method Based on Bispectrum and DNN
AU - Yu, Jiang
AU - He, Zunwen
AU - Zhang, Yan
N1 - Publisher Copyright:
© 2020, Springer Nature Singapore Pte Ltd.
PY - 2020
Y1 - 2020
N2 - In this paper, we propose a new method for modulation recognition of received digital signals using bispectrum and AlexNet. The bispectrum analysis is used to generate the feature images, AlexNet, as a widely used deep neural network (DNN), is used as the classifier. It is able to classify six common digital communication signals, including 2ASK, 4ASK, 2FSK, 4FSK, 2PSK and 4PSK. Compared to the traditional decision-theoretic methods, the proposed method needs no prior information for the received signals. The numerical results indicate that this method is more robust and effective than the classical decision theory and its improved algorithm, particularly when the signal-to-noise ratio (SNR) is low. It is shown that the success rate of 90% can be achieved when the SNR is greater than or equal to 3 dB.
AB - In this paper, we propose a new method for modulation recognition of received digital signals using bispectrum and AlexNet. The bispectrum analysis is used to generate the feature images, AlexNet, as a widely used deep neural network (DNN), is used as the classifier. It is able to classify six common digital communication signals, including 2ASK, 4ASK, 2FSK, 4FSK, 2PSK and 4PSK. Compared to the traditional decision-theoretic methods, the proposed method needs no prior information for the received signals. The numerical results indicate that this method is more robust and effective than the classical decision theory and its improved algorithm, particularly when the signal-to-noise ratio (SNR) is low. It is shown that the success rate of 90% can be achieved when the SNR is greater than or equal to 3 dB.
KW - AlexNet
KW - Bispectrum
KW - CNN
KW - DNN
KW - Modulation recognition
UR - http://www.scopus.com/inward/record.url?scp=85071495765&partnerID=8YFLogxK
U2 - 10.1007/978-981-13-6504-1_108
DO - 10.1007/978-981-13-6504-1_108
M3 - Conference contribution
AN - SCOPUS:85071495765
SN - 9789811365034
T3 - Lecture Notes in Electrical Engineering
SP - 898
EP - 906
BT - Communications, Signal Processing, and Systems - Proceedings of the 2018 CSPS Volume II
A2 - Liang, Qilian
A2 - Liu, Xin
A2 - Na, Zhenyu
A2 - Wang, Wei
A2 - Mu, Jiasong
A2 - Zhang, Baoju
PB - Springer Verlag
T2 - International Conference on Communications, Signal Processing, and Systems, CSPS 2018
Y2 - 14 July 2018 through 16 July 2018
ER -