Emerging Technologies for 6G Non-Terrestrial-Networks: From Academia to Industrial Applications
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Abstract (arXiv)
Terrestrial networks form the fundamental infrastructure of modern communication systems, serving more than 4 billion users globally. However, terrestrial networks are facing a wide range of challenges, from coverage and reliability to interference and congestion. As the demands of the 6G era are expected to be much higher, it is crucial to address these challenges to ensure a robust and efficient communication infrastructure for the future. To address these problems, Non-terrestrial Network (NTN) has emerged to be a promising solution. NTNs are communication networks that leverage airborne (e.g., unmanned aerial vehicles) and spaceborne vehicles (e.g., satellites) to facilitate ultra-reliable communications and connectivity with high data rates and low latency over expansive regions. This article aims to provide a comprehensive survey on the utilization of network slicing, Artificial Intelligence/Machine Learning (AI/ML), and Open Radio Access Network (ORAN) to address diverse challenges of NTNs from the perspectives of both academia and industry. Particularly, we first provide an in-depth tutorial on NTN and the key enabling technologies including network slicing, AI/ML, and ORAN. Then, we provide a comprehensive survey on how network slicing and AI/ML have been leveraged to overcome the challenges that NTNs are facing. Moreover, we present how ORAN can be utilized for NTNs. Finally, we highlight important challenges, open issues, and future research directions of NTN in the 6G era.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Mar 2024.
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Citing papers per year: 2025: 2; 2026: 3.
5 papers.
- 2025AI-Native Open RAN for Non-Terrestrial Networks: An Overviewcited by 1
- 2025Fed-KAN: Federated Learning with Kolmogorov-Arnold Networks for Traffic Predictioncited by 0
- 2026AI Lifecycle-Aware Feasibility Framework for Split-RIC Orchestration in NTN O-RANcited by 0
- 2026Digital Twin-Assisted Adaptive Multi-Agent DRL for Intelligent Spectrum and Resource Management in Open-RAN UAV-Enabled 6G Networkscited by 0
- 2026Multi-Agent DRL for QoS and Energy Optimization in RIS-Enabled Open-RAN Industrial 6G TN/NTN Networkscited by 0
- 2026Multi-Agent DRL for QoS and Energy Optimization in RIS-Enabled Open-RAN Industrial 6G TN/NTN Networks31 May 2026
- 2026Digital Twin-Assisted Adaptive Multi-Agent DRL for Intelligent Spectrum and Resource Management in Open-RAN UAV-Enabled 6G Networks31 May 2026
- 2026AI Lifecycle-Aware Feasibility Framework for Split-RIC Orchestration in NTN O-RAN24 Mar 2026
- 2025AI-Native Open RAN for Non-Terrestrial Networks: An Overview16 Jul 2025
- 2025Fed-KAN: Federated Learning with Kolmogorov-Arnold Networks for Traffic Prediction28 Feb 2025