Decentralized Learning-based Scenario Identification Method for Intelligent Vehicular Communications

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

研究成果: Conference contribution

抄録

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%.

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

フィンガープリント

「Decentralized Learning-based Scenario Identification Method for Intelligent Vehicular 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