Revolutionizing Future Connectivity: A Contemporary Survey on AI-empowered Satellite-based Non-Terrestrial Networks in 6G
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Abstract (arXiv)
Non-Terrestrial Networks (NTN) are expected to be a critical component of 6th Generation (6G) networks, providing ubiquitous, continuous, and scalable services. Satellites emerge as the primary enabler for NTN, leveraging their extensive coverage, stable orbits, scalability, and adherence to international regulations. However, satellite-based NTN presents unique challenges, including long propagation delay, high Doppler shift, frequent handovers, spectrum sharing complexities, and intricate beam and resource allocation, among others. The integration of NTNs into existing terrestrial networks in 6G introduces a range of novel challenges, including task offloading, network routing, network slicing, and many more. To tackle all these obstacles, this paper proposes Artificial Intelligence (AI) as a promising solution, harnessing its ability to capture intricate correlations among diverse network parameters. We begin by providing a comprehensive background on NTN and AI, highlighting the potential of AI techniques in addressing various NTN challenges. Next, we present an overview of existing works, emphasizing AI as an enabling tool for satellite-based NTN, and explore potential research directions. Furthermore, we discuss ongoing research efforts that aim to enable AI in satellite-based NTN through software-defined implementations, while also discussing the associated challenges. Finally, we conclude by providing insights and recommendations for enabling AI-driven satellite-based NTN in future 6G networks.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Mar 2023.
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Citing papers per year: 2024: 3; 2025: 6; 2026: 3.
12 papers.
- 2024Continual Deep Reinforcement Learning for Decentralized Satellite Routingcited by 6
- 2024On the Interplay of Artificial Intelligence and Space-Air-Ground Integrated Networks: A Surveycited by 5
- 2025Space-O-RAN: Enabling Intelligent, Open, and Interoperable Non Terrestrial Networks in 6Gcited by 4
- 2024Enhancing Spectrum Efficiency in 6G Satellite Networks: A GAIL-Powered Policy Learning via Asynchronous Federated Inverse Reinforcement Learningcited by 2
- 2025A Semantic-Loss Function Modeling Framework With Task-Oriented Machine Learning Perspectivescited by 2
- 2025Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approachescited by 2
- 2025AI-Native Open RAN for Non-Terrestrial Networks: An Overviewcited by 1
- 2025Bridging Earth and Space: A Survey on HAPS for Non-Terrestrial Networkscited by 1
- 2025Hierarchical Task Offloading and Trajectory Optimization in Low-Altitude Intelligent Networks Via Auction and Diffusion-based MARLcited by 1
- 2026Efficient Cross-View Localization in 6G Space-Air-Ground Integrated Networkcited by 0
- 2026Multi-Agent Reinforcement Learning Counteracts Delayed CSI in Multi-Satellite Systemscited by 0
- 2026On-Orbit Space AI: Federated, Multi-Agent, and Collaborative Algorithms for Satellite Constellationscited by 0
- 2026On-Orbit Space AI: Federated, Multi-Agent, and Collaborative Algorithms for Satellite Constellations15 Apr 2026
- 2026Multi-Agent Reinforcement Learning Counteracts Delayed CSI in Multi-Satellite Systems17 Mar 2026
- 2026Efficient Cross-View Localization in 6G Space-Air-Ground Integrated Network12 Mar 2026
- 2025Hierarchical Task Offloading and Trajectory Optimization in Low-Altitude Intelligent Networks Via Auction and Diffusion-based MARL5 Dec 2025
- 2025Bridging Earth and Space: A Survey on HAPS for Non-Terrestrial Networks22 Oct 2025
- 2025Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approaches19 Jul 2025
- 2025AI-Native Open RAN for Non-Terrestrial Networks: An Overview16 Jul 2025
- 2025A Semantic-Loss Function Modeling Framework With Task-Oriented Machine Learning Perspectives12 Mar 2025
- 2025Space-O-RAN: Enabling Intelligent, Open, and Interoperable Non Terrestrial Networks in 6G21 Feb 2025
- 2024Enhancing Spectrum Efficiency in 6G Satellite Networks: A GAIL-Powered Policy Learning via Asynchronous Federated Inverse Reinforcement Learning27 Sep 2024
- 2024Continual Deep Reinforcement Learning for Decentralized Satellite Routing20 May 2024
- 2024On the Interplay of Artificial Intelligence and Space-Air-Ground Integrated Networks: A Survey20 Jan 2024