# On the Interplay of Artificial Intelligence and Space-Air-Ground Integrated Networks: A Survey

arXiv: https://arxiv.org/abs/2402.00881 · HTML: https://mlsanity.com/paper/2402.00881/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 417 references that are in the map.
- First posted on arXiv 20 Jan 2024.

## Builds on (most cited first, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Tutorial on Variational Autoencoders](https://mlsanity.com/paper/1606.05908/) (2016)
- [Cooperative and Distributed Reinforcement Learning of Drones for Field Coverage](https://mlsanity.com/paper/1803.07250/) (2018)
- [Revolutionizing Future Connectivity: A Contemporary Survey on AI-empowered Satellite-based Non-Terrestrial Networks in 6G](https://mlsanity.com/paper/2303.01633/) (2023)
- [Spectrum Prediction and Interference Detection for Satellite Communications](https://mlsanity.com/paper/1912.04716/) (2019)
- [Deep Reinforcement Learning for Interference Management in UAV-based 3D Networks: Potentials and Challenges](https://mlsanity.com/paper/2305.07069/) (2023)
- [Deep Reinforcement Learning for Unmanned Aerial Vehicle-Assisted Vehicular Networks](https://mlsanity.com/paper/1906.05015/) (2019)
- [Predictive Estimation of the Optimal Signal Strength from Unmanned Aerial Vehicle over Internet of Things Using ANN](https://mlsanity.com/paper/1805.07614/) (2018)
- [A Key 6G Challenge and Opportunity -- Connecting the Remaining 4 Billions: A Survey on Rural Connectivity](https://mlsanity.com/paper/1906.11541/) (2019)
- [Exploiting Hybrid Terrestrial/LEO Satellite Systems for Rural Connectivity](https://mlsanity.com/paper/2311.02591/) (2023)

## Built on by (most cited first, 5 of 5)

- [UAV-assisted Unbiased Hierarchical Federated Learning: Performance and Convergence Analysis](https://mlsanity.com/paper/2407.07739/) (2024)
- [Personalized Federated Learning for Cellular VR: Online Learning and Dynamic Caching](https://mlsanity.com/paper/2501.11745/) (2025)
- [Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approaches](https://mlsanity.com/paper/2507.14633/) (2025)
- [Bridging Earth and Space: A Survey on HAPS for Non-Terrestrial Networks](https://mlsanity.com/paper/2510.19731/) (2025)
- [VERA: Verifiable Feasibility Representations with Counterfactual Credit for Constrained Multi-Agent Control](https://mlsanity.com/paper/2609.38889/) (2026)

## Abstract (arXiv)

Space-Air-Ground Integrated Networks (SAGINs), which incorporate space and aerial networks with terrestrial wireless systems, are vital enablers of the emerging sixth-generation (6G) wireless networks. Besides bringing significant benefits to various applications and services, SAGINs are envisioned to extend high-speed broadband coverage to remote areas, such as small towns or mining sites, or areas where terrestrial infrastructure cannot reach, such as airplanes or maritime use cases. However, due to the limited power and storage resources, as well as other constraints introduced by the design of terrestrial networks, SAGINs must be intelligently configured and controlled to satisfy the envisioned requirements. Meanwhile, Artificial Intelligence (AI) is another critical enabler of 6G. Due to massive amounts of available data, AI has been leveraged to address pressing challenges of current and future wireless networks. By adding AI and facilitating the decision-making and prediction procedures, SAGINs can effectively adapt to their surrounding environment, thus enhancing the performance of various metrics. In this work, we aim to investigate the interplay of AI and SAGINs by providing a holistic overview of state-of-the-art research in AI-enabled SAGINs. Specifically, we present a comprehensive overview of some potential applications of AI in SAGINs. We also cover open issues in employing AI and detail the contributions of SAGINs in the development of AI. Finally, we highlight some limitations of the existing research works and outline potential future research directions.