Lightweight Network Design Based on ResNet Structure for Modulation Recognition

Xiao Lu, Mengyuan Tao, Xue Fu, Guan Gui, Tomoaki Ohtsuki, Hikmet Sari

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2021 IEEE 94th Vehicular Technology Conference, VTC 2021-Fall - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781665413688
DOI
出版ステータスPublished - 2021
外部発表はい
イベント94th IEEE Vehicular Technology Conference, VTC 2021-Fall - Virtual, Online, United States
継続期間: 2021 9月 272021 9月 30

出版物シリーズ

名前IEEE Vehicular Technology Conference
2021-September
ISSN(印刷版)1550-2252

Conference

Conference94th IEEE Vehicular Technology Conference, VTC 2021-Fall
国/地域United States
CityVirtual, Online
Period21/9/2721/9/30

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 電子工学および電気工学
  • 応用数学

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