Decentralized Learning-based Scenario Identification Method for Intelligent Vehicular Communications

Yaru Zhou, Yu Wangt, Pengfei Liu, Jie Yang, Tomoaki Ohtsuki, Hikmet Sari

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


Scenario identification (SCI) is one of key techniques for intelligent vehicular communications (IVC) to maintain an effective and reliable operating state. Based on the deep learning (DL), it is a hotspot to identify scenarios of wireless communication using the characteristic quantity inherent in wireless channels. This paper proposes a decentralized learning-based SCI (DecentSCI) for IVC, relying on the algorithm of lightweight and model aggregation. By improving training efficiency and meanwhile reducing model complexity, the proposed method achieves low computing and communication, which is applicable for vehicular devices. Simulation results show that the training efficiency is upgraded by 97.15% and the model complexity is decreased by 90.25% at the cost of slight performance loss, i.e., 0.15%.

Original languageEnglish
Title of host publication2021 IEEE 94th Vehicular Technology Conference, VTC 2021-Fall - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665413688
Publication statusPublished - 2021
Externally publishedYes
Event94th IEEE Vehicular Technology Conference, VTC 2021-Fall - Virtual, Online, United States
Duration: 2021 Sept 272021 Sept 30

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252


Conference94th IEEE Vehicular Technology Conference, VTC 2021-Fall
Country/TerritoryUnited States
CityVirtual, Online


  • Scenario identification
  • decentralized learning
  • lightweight convolutional neural network
  • model aggregation

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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