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Robust Wireless Fronthauling Methods for Decentralized Deep Learning in Fog-RAN

  • Hoon Lee
  • , Junbeom Kim
  • , Seok Hwan Park
  • Pukyong National University
  • Jeonbuk National University

Research output: Contribution to conferenceConference paperpeer-review

Abstract

This paper designs wireless fronthauling techniques for deep learning (DL) enabled fog radio access networks (F-RANs) where computation and communication processes at a cloud and edge nodes (ENs) are carried out by deep neural networks (DNNs). Coordination among ENs and the cloud is realized by wireless fronthaul links, which incurs undesired randomness in forwardpass calculations of DNNs. To address this issue, we propose a robust training strategy whereby a group of DNNs can mitigate the impairment from fronthaul fading and additive noise. Numerical results demonstrate the superiority of the proposed robust wireless fronthauling scheme.

Original languageEnglish
Title of host publicationICTC 2021 - 12th International Conference on ICT Convergence
Subtitle of host publicationBeyond the Pandemic Era with ICT Convergence Innovation
PublisherIEEE Computer Society
Pages315-317
Number of pages3
ISBN (Electronic)9781665423830
DOIs
StatePublished - 2021
Event12th International Conference on Information and Communication Technology Convergence, ICTC 2021 - Jeju Island, Korea, Republic of
Duration: 2021.10.202021.10.22

Publication series

NameInternational Conference on ICT Convergence
Volume2021-October
ISSN (Print)2162-1233
ISSN (Electronic)2162-1241

Conference

Conference12th International Conference on Information and Communication Technology Convergence, ICTC 2021
Country/TerritoryKorea, Republic of
CityJeju Island
Period21.10.2021.10.22

Keywords

  • Deep learning
  • fog radio access networks
  • wireless fronthauling

Quacquarelli Symonds(QS) Subject Topics

  • Computer Science & Information Systems

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