# Revolutionizing Future Connectivity: A Contemporary Survey on AI-empowered Satellite-based Non-Terrestrial Networks in 6G

arXiv: https://arxiv.org/abs/2303.01633 · HTML: https://mlsanity.com/paper/2303.01633/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2023.

## Built on by (most cited first, 13 of 13)

- [Collaborative Ground-Space Communications via Evolutionary Multi-objective Deep Reinforcement Learning](https://mlsanity.com/paper/2404.07450/) (2024)
- [Continual Deep Reinforcement Learning for Decentralized Satellite Routing](https://mlsanity.com/paper/2405.12308/) (2024)
- [On the Interplay of Artificial Intelligence and Space-Air-Ground Integrated Networks: A Survey](https://mlsanity.com/paper/2402.00881/) (2024)
- [Space-O-RAN: Enabling Intelligent, Open, and Interoperable Non Terrestrial Networks in 6G](https://mlsanity.com/paper/2502.15936/) (2025)
- [Enhancing Spectrum Efficiency in 6G Satellite Networks: A GAIL-Powered Policy Learning via Asynchronous Federated Inverse Reinforcement Learning](https://mlsanity.com/paper/2409.18718/) (2024)
- [A Semantic-Loss Function Modeling Framework With Task-Oriented Machine Learning Perspectives](https://mlsanity.com/paper/2503.09903/) (2025)
- [Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approaches](https://mlsanity.com/paper/2507.14633/) (2025)
- [AI-Native Open RAN for Non-Terrestrial Networks: An Overview](https://mlsanity.com/paper/2507.11935/) (2025)
- [Bridging Earth and Space: A Survey on HAPS for Non-Terrestrial Networks](https://mlsanity.com/paper/2510.19731/) (2025)
- [Hierarchical Task Offloading and Trajectory Optimization in Low-Altitude Intelligent Networks Via Auction and Diffusion-based MARL](https://mlsanity.com/paper/2512.11862/) (2025)
- [Efficient Cross-View Localization in 6G Space-Air-Ground Integrated Network](https://mlsanity.com/paper/2603.11398/) (2026)
- [Multi-Agent Reinforcement Learning Counteracts Delayed CSI in Multi-Satellite Systems](https://mlsanity.com/paper/2603.16470/) (2026)
- [On-Orbit Space AI: Federated, Multi-Agent, and Collaborative Algorithms for Satellite Constellations](https://mlsanity.com/paper/2604.16518/) (2026)

## 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.