AI and Digital Twin-based Multi-Operator Collaboration for 6G Use Cases

Cebecioglu, Berna Bulut and Bipon, Md Abrar Jahin Almazi and Soleymani, Seyed and Abozariba, Raouf and Aneiba, Adel and Han, Qingson and Xiao, Sa and Zhang, Jintao (2026) AI and Digital Twin-based Multi-Operator Collaboration for 6G Use Cases. IEEE Communications Standards Magazine. ISSN 2471-2825 (In Press)

Text Main.pdf - Accepted Version Restricted to Repository staff only Download (1MB) | Request a copy

Abstract

Sixth-generation (6G) networks must support immersive and mission-critical services that impose extreme and simultaneous requirements on data rate, latency, reliability, and connectivity. Meeting these stringent demands challenges the capabilities of traditional single-operator network operation, necessitating architectural enhancements. To address this, this paper investigates multi-operator collaboration as a scalable paradigm for 6G, enabled by the synergy of artificial intelligence (AI) and digital twins (DTs). We propose a novel, unified framework: per-operator DTs facilitate high-fidelity, predictive network modeling, while a federated multi-agent deep reinforcement learning scheme enables robust, privacy-preserving cross-operator optimization. A real-world case study, utilizing extensive measurement data from four major mobile network operators (MNOs) in the United Kingdom, demonstrates the framework’s efficacy. Results show that the proposed multi-MNO collaborative approach significantly reduces quality-ofservice violations and improves achievable data rates compared to standalone deployments, particularly under the high-throughput demands characteristic of immersive applications. Finally, we conclude by discussing the key challenges and future research directions essential for realizing practical, AI-native 6G networks.

Item Type:	Article
Dates:	Date Event 22 April 2026 Accepted
Uncontrolled Keywords:	Multi-operator collaboration, Federated learning, Machine learning, Digital twin, 6G.
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Architecture, Built Environment, Computing and Engineering > Computer Science
Depositing User:	Gemma Tonks
Date Deposited:	14 May 2026 10:45
Last Modified:	14 May 2026 10:45
URI:	https://www.open-access.bcu.ac.uk/id/eprint/17041

Actions (login required)

View Item