Differentiable architecture search-based automatic modulation classification

Xun Wei, Wang Luo, Xixi Zhang, Jie Yang, Guan Gui, Tomoaki Ohtsuki

研究成果: Conference contribution

4 被引用数 (Scopus)

抄録

Automatic modulation classification (AMC) is an essential and meaningful technology in the development of cognitive radio. It can judge the modulation mode according to the signal acquired by the receiver. In recent years, the deep learning (DL) method has been used to take the place of modulation signal recognition based on decision theory and pattern recognition, which has achieved very effective results. The development of the neural network classification model focuses on architectural engineering. Discovering state-of-the-art neural network architectures requires substantial prior knowledge and effort of human experts. Neural architecture search (NAS) can be viewed as a subdomain of automatic machine learning (AutoML), which uses a neural network to automatically adjust the structures and parameters to obtain a network that researchers need by following search strategies that maximize performance. In this paper, we propose a differentiable architecture search (DARTS) based AMC method. In addition, we also consider six other methods, including convolutional neural network (CNN), simple recurrent unit (SRU), a convolutional-recurrent neural network (CRFN-CSS), Residual Networks (ResNet), Inception Modules (Inception) and MobileNet. Simulation results show that the proposed method can achieve the optimal classification accuracy at low parameters and floating-point operations (FLOPs) without manual architecture engineering.

本文言語English
ホスト出版物のタイトル2021 IEEE Wireless Communications and Networking Conference, WCNC 2021
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728195056
DOI
出版ステータスPublished - 2021
イベント2021 IEEE Wireless Communications and Networking Conference, WCNC 2021 - Nanjing, China
継続期間: 2021 3月 292021 4月 1

出版物シリーズ

名前IEEE Wireless Communications and Networking Conference, WCNC
2021-March
ISSN(印刷版)1525-3511

Conference

Conference2021 IEEE Wireless Communications and Networking Conference, WCNC 2021
国/地域China
CityNanjing
Period21/3/2921/4/1

ASJC Scopus subject areas

  • 工学(全般)

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