Artificial Intelligence for Satellite Communication and Non-Terrestrial Networks: A Survey
Not on the map. Why?
Abstract (arXiv)
This paper surveys the application and development of Artificial Intelligence (AI) in Satellite Communication (SatCom) and Non-Terrestrial Networks (NTN). We first present a comprehensive list of use cases, the relative challenges and the main AI tools capable of addressing those challenges. For each use case, we present the main motivation, a system description, the available non-AI solutions and the potential benefits and available works using AI. We also discuss the pros and cons of an on-board and on-ground AI-based architecture, and we revise the current commercial and research activities relevant to this topic. Next, we describe the state-of-the-art hardware solutions for developing ML in real satellite systems. Finally, we discuss the long-term developments of AI in the SatCom and NTN sectors and potential research directions. This paper provides a comprehensive and up-to-date overview of the opportunities and challenges offered by AI to improve the performance and efficiency of NTNs.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Apr 2023.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2024: 3; 2025: 2; 2026: 1.
6 papers.
- 2024Onboard Satellite Image Classification for Earth Observation: A Comparative Study of ViT Modelscited by 7
- 2024Energy-Aware Federated Learning in Satellite Constellationscited by 3
- 2024Semantic Knowledge Distillation for Onboard Satellite Earth Observation Image Classificationcited by 2
- 2025A Semantic-Loss Function Modeling Framework With Task-Oriented Machine Learning Perspectivescited by 2
- 2025Bridging Earth and Space: A Survey on HAPS for Non-Terrestrial Networkscited by 1
- 2026Toward a Unified Semantic Loss Model for Deep JSCC-based Transmission of EO Imagerycited by 0
- 2026Toward a Unified Semantic Loss Model for Deep JSCC-based Transmission of EO Imagery28 Jan 2026
- 2025Bridging Earth and Space: A Survey on HAPS for Non-Terrestrial Networks22 Oct 2025
- 2025A Semantic-Loss Function Modeling Framework With Task-Oriented Machine Learning Perspectives12 Mar 2025
- 2024Semantic Knowledge Distillation for Onboard Satellite Earth Observation Image Classification31 Oct 2024
- 2024Energy-Aware Federated Learning in Satellite Constellations23 Sep 2024
- 2024Onboard Satellite Image Classification for Earth Observation: A Comparative Study of ViT Models5 Sep 2024