Lightweight network and model aggregation for automatic modulation classification in wireless communications

Xue Fu, Guan Gui, Yu Wang, Tomoaki Ohtsuki, Bamidele Adebisi, Haris Gacanin, Fumiyuki Adachi

研究成果: Conference contribution

抄録

This paper proposes a decentralized automatic modulation classification (DecentAMC) method using light network and model aggregation. Specifically, the lightweight network is designed by separable convolution neural network (S-CNN), in which the separable convolution layer is utilized to replace the standard convolution layer and most of the fully connected layers are cut off, the model aggregation is realized by a central device (CD) for edge device (ED) model weights aggregation and multiple EDs for ED model training. Simulation results show that the model complexity of S-CNN is decreased by about 94% while the average CCP is degraded by less than 1% when compared with CNN and that the proposed AMC method improves the training efficiency when compared with the centralized AMC (CentAMC) using S-CNN.

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)

フィンガープリント

「Lightweight network and model aggregation for automatic modulation classification in wireless communications」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル