ML-based estimation of the number of devices in industrial networks using unlicensed bands: (Best workshop paper)

Oluwatobi Baiyekusi, Haeyoung Lee, Klaus Moessner

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

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Abstract

Advanced automation is being adopted by manu-facturing facilities and wireless technologies are set to be a key component in driving the factories of the future. It is expected that private cellular networks and WLAN technologies would be deployed for smart factory operations. Since both wireless technologies can operate on the same channel in unlicensed bands, then efficient resource sharing becomes important. When multiple devices compete for the resource, the estimation of number of devices contending for the channel resource can help the design of an efficient resource sharing scheme. This paper aims to address the challenge of estimating the number of factory devices contending to transmit over the unlicensed channel. We adopt three machine learning (ML) techniques and develop a novel device number estimation system by collating and analysing the idle-time interval between transmission across the channel. By using NS-3 simulation, the performance of the proposed estimation approach is evaluated. The results presented reveal the significance of the chosen features and performance of each ML algorithm used.
Original languageEnglish
Title of host publication2022 13th International Conference on Information and Communication Technology Convergence (ICTC)
Subtitle of host publicationAccelerating Digital Transformation with ICT Innovation
Place of PublicationJeju Island, Korea, Republic of
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages519-524
Number of pages6
ISBN (Electronic)978-1-6654-9939-2
ISBN (Print)978-1-6654-9940-8
DOIs
Publication statusPublished - 21 Oct 2022
Event2022 13th International Conference on Information and Communication Technology Convergence (ICTC): “Accelerating Digital Transformation with ICT Innovation” - Jeju Island, Korea, Democratic People's Republic of
Duration: 19 Oct 202221 Oct 2022
Conference number: 13
https://ieeexplore.ieee.org/xpl/conhome/9952188/proceeding

Publication series

NameInternational Conference on ICT Convergence
Volume2022-October
ISSN (Print)2162-1233
ISSN (Electronic)2162-1241

Conference

Conference2022 13th International Conference on Information and Communication Technology Convergence (ICTC)
Abbreviated titleICTC 2022
Country/TerritoryKorea, Democratic People's Republic of
CityJeju Island
Period19/10/2221/10/22
Internet address

Keywords

  • Radio frequency
  • Performance evaluation
  • Wireless communication
  • Maximum likelihood estimation
  • Computational modeling
  • Channel estimation
  • Prediction algorithms
  • unlicensed band
  • Machine learning
  • number of device estimation
  • smart factory

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