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AI-Assisted Improved Service Provisioning for Low-Latency XR over 5G NR
Indian Institute of Technology Kharagpur, G. S. Sanyal School of Telecommunications, Kharagpur, India.
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.ORCID iD: 0000-0002-2246-9905
Indian Institute of Technology Kharagpur, G. S. Sanyal School of Telecommunications, Kharagpur, India.
Indian Institute of Technology Kharagpur, G. S. Sanyal School of Telecommunications, Kharagpur, India.
2024 (English)In: IEEE Networking Letters, E-ISSN 2576-3156, Vol. 6, no 1, p. 31-35Article in journal (Refereed) Published
Abstract [en]

Extended Reality (XR) is one of the most important 5G/6G media applications that will fundamentally transform human interactions. However, ensuring low latency, high data rate, and reliability to support XR services poses significant challenges. This letter presents a novel AI-assisted service provisioning scheme that leverages predicted frames for processing rather than relying solely on actual frames. This method virtually increases the network delay budget and consequently improves service provisioning, albeit at the expense of minor prediction errors. The proposed scheme is validated by extensive simulations demonstrating a multi-fold increase in supported XR users and also provides crucial network design insights.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2024. Vol. 6, no 1, p. 31-35
Keywords [en]
5G NR, AI, AR, Extended reality (XR), VR
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-356974DOI: 10.1109/LNET.2023.3316390Scopus ID: 2-s2.0-85209468819OAI: oai:DiVA.org:kth-356974DiVA, id: diva2:1916681
Note

QC 20241129

Available from: 2024-11-28 Created: 2024-11-28 Last updated: 2026-04-20Bibliographically approved

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