IBN-ZTSA: AI-IBN for Zero Touch Service Automation of B5G Terrestrial and Non-Terrestrial Networks
Accommodating novel and diverse services with different quality of service requirements (QoS), including Web 3.0, metaverse, AR/VR, smart industries, smart hospitals, etc., is very challenging for Beyond 5G (B5G) networks. These services require high-speed, low latency, and seamless connectivity. Conversely, network slicing in B5G networks is the best candidate to support these services by providing dedicated resources. However, automatically orchestrating and managing network slicing for these services is challenging. Therefore, this article presents a closed-loop and zero-touch Intent-Based Networking (IBN) architecture named IBN-ZTSA for network slice design and orchestration over multidomain infrastructure terrestrial and non-terrestrial networks (NTN). IBN-ZTSA automatically performs commissioning, activating, monitoring, and decommissioning end-to-end (e2e) slices. Moreover, we have integrated an Artificial Intelligence (AI) empowered network data analytics mechanism with IBN to enable network intelligence for proactive management and control. Finally, we performed several experiments by creating end-to-end (e2e) slices through the IBN-ZTSA mechanism, which shows satisfactory performance in automated service provisioning, resource allocation, and resource management.
Authors
- Yeongpil Cho (ORCID: https://orcid.org/0000-0001-7842-1719)
- Jae-Hyung Yoo
- Khizar Abbas (ORCID: https://orcid.org/0000-0002-2432-1357)
- Wang‐Cheol Song (ORCID: https://orcid.org/0000-0002-7411-5316)
- James Won‐Ki Hong (ORCID: https://orcid.org/0000-0003-2853-7734)
- Muhammad Afaq (ORCID: https://orcid.org/0000-0001-8678-0754)
- Ali Nauman (ORCID: https://orcid.org/0000-0002-2133-5286)
Institutions
- Pohang University of Science and Technology (KR)
- Gachon University (KR)
- King Fahd University of Petroleum and Minerals (SA)
- Hanyang University (KR)
- Yeungnam University (KR)
- Jeju National University (KR)
Publication Details
- Journal
- IEEE Communications Standards Magazine
- Published
- 2025-05-19
- DOI
- https://doi.org/10.1109/mcomstd.2025.3568906
- Citations
- 2
- Primary Topic
- IoT and Edge/Fog Computing
- Type
- article
- Field-Weighted Citation Impact
- 2.97