A ML-based Spectrum Sharing Technique for Time-Sensitive Applications in Industrial Scenarios

Oluwatobi  Baiyekusi; Haitham Mahmoud; De Mi; Junaid Arshad; Femi Adeyemi-Ejeye; Haeyoung Lee

doi:10.1109/IWCMC61514.2024.10592619

A ML-based Spectrum Sharing Technique for Time-Sensitive Applications in Industrial Scenarios

Oluwatobi Baiyekusi, Haitham Mahmoud, De Mi, Junaid Arshad, Femi Adeyemi-Ejeye, Haeyoung Lee

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Abstract

Industry 4.0, driven by enhanced connectivity by wireless technologies such as 5G and Wi-Fi 6, fosters flexible industrial scenarios for high-yield production and services. Private5G networks and 802.11ax networks in unlicensed spectrum offer very unique opportunities, however existing techniques limit the flexibility needed to serve diverse industrial use cases. In order to address a subset of these challenges, this paper offers a solution for time-sensitive application use cases. A new technique is proposed to enable data-driven operations through Machine Learning for technologies sharing unlicensed bands. This enables proportionate spectrum sharing informed by data to improve critical applications performance metrics. The results presented reveal improved performance to serve critical industrial operations, without degrading spectrum utilization.

Original language	English
Title of host publication	2024 International Wireless Communications and Mobile Computing (IWCMC)
Place of Publication	Ayia Napa, Cyprus
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	1-6
Number of pages	6
ISBN (Electronic)	979-8-3503-6126-1
DOIs	https://doi.org/10.1109/IWCMC61514.2024.10592619
Publication status	E-pub ahead of print - 17 Jul 2024
Event	IWCMC 2023: The 19th International Wireless Communications and Mobile Computing 2023 - Marrakesh, Morocco Duration: 19 Jun 2023 → 23 Jun 2023 Conference number: 19 https://www.comsoc.org/conferences-events/international-wireless-communications-and-mobile-computing-2023

Publication series

Name	International Wireless Communications and Mobile Computing
Publisher	IEEE
ISSN (Electronic)	2376-6506

Conference

Conference	IWCMC 2023: The 19th International Wireless Communications and Mobile Computing 2023
Abbreviated title	IWCMC 2023
Country/Territory	Morocco
City	Marrakesh
Period	19/06/23 → 23/06/23
Internet address	https://www.comsoc.org/conferences-events/international-wireless-communications-and-mobile-computing-2023

Keywords

5G
802.11ax
Spectrum Sharing
Contention Window
Time-Sensitive Applications

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10.1109/IWCMC61514.2024.10592619Licence: Unspecified

Accepted_Manuscript
© 2024 IEEE. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/IWCMC61514.2024.10592619
Accepted author manuscript, 248 KBLicence: CC BY

Cite this

Baiyekusi, O., Mahmoud, H., Mi, D., Arshad, J., Adeyemi-Ejeye, F., & Lee, H. (2024). A ML-based Spectrum Sharing Technique for Time-Sensitive Applications in Industrial Scenarios. In 2024 International Wireless Communications and Mobile Computing (IWCMC) (pp. 1-6). (International Wireless Communications and Mobile Computing). Institute of Electrical and Electronics Engineers (IEEE). Advance online publication. https://doi.org/10.1109/IWCMC61514.2024.10592619

Baiyekusi, Oluwatobi ; Mahmoud, Haitham ; Mi, De et al. / A ML-based Spectrum Sharing Technique for Time-Sensitive Applications in Industrial Scenarios. 2024 International Wireless Communications and Mobile Computing (IWCMC). Ayia Napa, Cyprus : Institute of Electrical and Electronics Engineers (IEEE), 2024. pp. 1-6 (International Wireless Communications and Mobile Computing).

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title = "A ML-based Spectrum Sharing Technique for Time-Sensitive Applications in Industrial Scenarios",

abstract = "Industry 4.0, driven by enhanced connectivity by wireless technologies such as 5G and Wi-Fi 6, fosters flexible industrial scenarios for high-yield production and services. Private5G networks and 802.11ax networks in unlicensed spectrum offer very unique opportunities, however existing techniques limit the flexibility needed to serve diverse industrial use cases. In order to address a subset of these challenges, this paper offers a solution for time-sensitive application use cases. A new technique is proposed to enable data-driven operations through Machine Learning for technologies sharing unlicensed bands. This enables proportionate spectrum sharing informed by data to improve critical applications performance metrics. The results presented reveal improved performance to serve critical industrial operations, without degrading spectrum utilization. ",

keywords = "5G, 802.11ax, Spectrum Sharing, Contention Window, Time-Sensitive Applications",

author = "Oluwatobi Baiyekusi and Haitham Mahmoud and De Mi and Junaid Arshad and Femi Adeyemi-Ejeye and Haeyoung Lee",

note = "{\textcopyright} 2024 IEEE. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/IWCMC61514.2024.10592619; IWCMC 2023: The 19th International Wireless Communications and Mobile Computing 2023, IWCMC 2023 ; Conference date: 19-06-2023 Through 23-06-2023",

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Baiyekusi, O, Mahmoud, H, Mi, D, Arshad, J, Adeyemi-Ejeye, F & Lee, H 2024, A ML-based Spectrum Sharing Technique for Time-Sensitive Applications in Industrial Scenarios. in 2024 International Wireless Communications and Mobile Computing (IWCMC). International Wireless Communications and Mobile Computing, Institute of Electrical and Electronics Engineers (IEEE), Ayia Napa, Cyprus, pp. 1-6, IWCMC 2023: The 19th International Wireless Communications and Mobile Computing 2023, Marrakesh, Morocco, 19/06/23. https://doi.org/10.1109/IWCMC61514.2024.10592619

A ML-based Spectrum Sharing Technique for Time-Sensitive Applications in Industrial Scenarios. / Baiyekusi, Oluwatobi ; Mahmoud, Haitham; Mi, De et al.
2024 International Wireless Communications and Mobile Computing (IWCMC). Ayia Napa, Cyprus: Institute of Electrical and Electronics Engineers (IEEE), 2024. p. 1-6 (International Wireless Communications and Mobile Computing).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Baiyekusi, Oluwatobi

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AU - Arshad, Junaid

AU - Adeyemi-Ejeye, Femi

AU - Lee, Haeyoung

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N2 - Industry 4.0, driven by enhanced connectivity by wireless technologies such as 5G and Wi-Fi 6, fosters flexible industrial scenarios for high-yield production and services. Private5G networks and 802.11ax networks in unlicensed spectrum offer very unique opportunities, however existing techniques limit the flexibility needed to serve diverse industrial use cases. In order to address a subset of these challenges, this paper offers a solution for time-sensitive application use cases. A new technique is proposed to enable data-driven operations through Machine Learning for technologies sharing unlicensed bands. This enables proportionate spectrum sharing informed by data to improve critical applications performance metrics. The results presented reveal improved performance to serve critical industrial operations, without degrading spectrum utilization.

AB - Industry 4.0, driven by enhanced connectivity by wireless technologies such as 5G and Wi-Fi 6, fosters flexible industrial scenarios for high-yield production and services. Private5G networks and 802.11ax networks in unlicensed spectrum offer very unique opportunities, however existing techniques limit the flexibility needed to serve diverse industrial use cases. In order to address a subset of these challenges, this paper offers a solution for time-sensitive application use cases. A new technique is proposed to enable data-driven operations through Machine Learning for technologies sharing unlicensed bands. This enables proportionate spectrum sharing informed by data to improve critical applications performance metrics. The results presented reveal improved performance to serve critical industrial operations, without degrading spectrum utilization.

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M3 - Conference contribution

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BT - 2024 International Wireless Communications and Mobile Computing (IWCMC)

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T2 - IWCMC 2023: The 19th International Wireless Communications and Mobile Computing 2023

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Baiyekusi O, Mahmoud H, Mi D, Arshad J, Adeyemi-Ejeye F, Lee H. A ML-based Spectrum Sharing Technique for Time-Sensitive Applications in Industrial Scenarios. In 2024 International Wireless Communications and Mobile Computing (IWCMC). Ayia Napa, Cyprus: Institute of Electrical and Electronics Engineers (IEEE). 2024. p. 1-6. (International Wireless Communications and Mobile Computing). Epub 2024 Jul 17. doi: 10.1109/IWCMC61514.2024.10592619

A ML-based Spectrum Sharing Technique for Time-Sensitive Applications in Industrial Scenarios

Abstract

Publication series

Conference

Keywords

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Cite this