TY - GEN
T1 - Lightweight Network Design Based on ResNet Structure for Modulation Recognition
AU - Lu, Xiao
AU - Tao, Mengyuan
AU - Fu, Xue
AU - Gui, Guan
AU - Ohtsuki, Tomoaki
AU - Sari, Hikmet
N1 - Funding Information:
Universal Wireless Communications (BUPT) of Ministry of Education of China under Grant KFKT-2020106.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - The problem of unknown modulation signal recognition has been received intensely attentions in next-generational intelligent wireless communications. The deep learning (DL) has been widely used in unknown modulation signal recognition due to its excellent performance in solving classification problems and the DL-based automatic modulation classification (AMC) had been proposed. However, DL-based AMC method usually has high space complexity and computational complexity, which limits DL-based AMC to miniaturized devices with limited storage and computing capability. Therefore, a lightweight residual neural network (LResNet) for AMC is proposed in this paper. The simulation results show that the model parameters of LResNet is about 4.8% of the traditional CNN network, and about 14.9% of the ResNet and the classification performance of LResNet improves more than 3% compared with the traditional CNN network and decreases less than 1.5% compared to the ResNet.
AB - The problem of unknown modulation signal recognition has been received intensely attentions in next-generational intelligent wireless communications. The deep learning (DL) has been widely used in unknown modulation signal recognition due to its excellent performance in solving classification problems and the DL-based automatic modulation classification (AMC) had been proposed. However, DL-based AMC method usually has high space complexity and computational complexity, which limits DL-based AMC to miniaturized devices with limited storage and computing capability. Therefore, a lightweight residual neural network (LResNet) for AMC is proposed in this paper. The simulation results show that the model parameters of LResNet is about 4.8% of the traditional CNN network, and about 14.9% of the ResNet and the classification performance of LResNet improves more than 3% compared with the traditional CNN network and decreases less than 1.5% compared to the ResNet.
KW - Automatic modulation classification (AMC)
KW - convolutional neural network (CNN)
KW - residual neural network (ResNet)
KW - separable convolution
UR - http://www.scopus.com/inward/record.url?scp=85123007504&partnerID=8YFLogxK
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U2 - 10.1109/VTC2021-Fall52928.2021.9625558
DO - 10.1109/VTC2021-Fall52928.2021.9625558
M3 - Conference contribution
AN - SCOPUS:85123007504
T3 - IEEE Vehicular Technology Conference
BT - 2021 IEEE 94th Vehicular Technology Conference, VTC 2021-Fall - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 94th IEEE Vehicular Technology Conference, VTC 2021-Fall
Y2 - 27 September 2021 through 30 September 2021
ER -