Shinde, S.S., Naseh, D. orcid.org/0009-0006-0767-7622 and Tarchi, D. (2025) ML-Based Intelligent O-RAN Control in 6G Integrated Terrestrial and Non-Terrestrial Networks. In: 2024 IEEE Globecom Workshops (GC Wkshps). 2024 IEEE Globecom Workshops (GC Wkshps), 08-12 Dec 2024, Cape Town, South Africa. . Institute of Electrical and Electronics Engineers (IEEE). ISBN: 979-8-3315-0568-4. ISSN: 2166-0069. EISSN: 2166-0077.
Abstract
The Open Radio Access Network (O-RAN) specification aims to define open, interoperable RAN interfaces with virtualized, intelligent RAN functions to support next-generation integrated Terrestrial and Non-Terrestrial Networks (T-NTN). The O-RAN specification introduces several options in which ML functional blocks can be distributed across several layers of the T-NTN infrastructure to enable intelligent and optimized RAN control mechanisms on different time scales. Machine Learning solutions can be adapted to T-NTN to enable efficient centralized and distributed intelligence solutions with specific performance requirements. We further include the possibility of considering multi-time-scale intelligence solutions over the NTN RAN through the distributed control architectures supported by the O-RAN technology.
Metadata
| Item Type: | Proceedings Paper |
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| Authors/Creators: |
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| Copyright, Publisher and Additional Information: | This is an author produced version of a proceedings paper published in 2024 IEEE Globecom Workshops (GC Wkshps), made available via the University of Leeds Research Outputs Policy under the terms of the Creative Commons Attribution License (CC-BY), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | Non-terrestrial Networks, O-RAN, Machine Learning, Distributed Machine Learning, 6G |
| Dates: |
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| Institution: | The University of Leeds |
| Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) |
| Date Deposited: | 12 Jun 2026 13:16 |
| Last Modified: | 15 Jun 2026 21:31 |
| Published Version: | https://ieeexplore.ieee.org/document/11101652 |
| Status: | Published |
| Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
| Identification Number: | 10.1109/gcwkshp64532.2024.11101652 |
| Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:241684 |
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Filename: _GC24WS_OJCOMS__Machine_Learning_for_Space_O_RAN.pdf
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