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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2021 IEEE Wireless Communications and Networking Conference, WCNC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728195056
DOIs
Publication statusPublished - 2021
Event2021 IEEE Wireless Communications and Networking Conference, WCNC 2021 - Nanjing, China
Duration: 2021 Mar 292021 Apr 1

Publication series

NameIEEE Wireless Communications and Networking Conference, WCNC
Volume2021-March
ISSN (Print)1525-3511

Conference

Conference2021 IEEE Wireless Communications and Networking Conference, WCNC 2021
Country/TerritoryChina
CityNanjing
Period21/3/2921/4/1

Keywords

  • Automatic modulation classification (AMC)
  • Lightweight network
  • Model aggregation

ASJC Scopus subject areas

  • Engineering(all)

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