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

Adilya Bakambekova, Nour Kouzayha, Tareq Al-Naffouri

TL;DR

The paper systematically surveys how AI techniques, notably reinforcement learning and deep learning, are applied across SAGINs—satellites, HAPS, UAVs, and terrestrial networks—to tackle beam management, routing, resource allocation, mobility, and security in 6G. It highlights how SAGINs also enable AI advances, through edge intelligence, federated learning, and distributed data collection, while enabling new AI-focused techniques such as analog over-the-air computation and RIS-assisted optimization. The work synthesizes cross-layer AI solutions, identifies open issues (security, privacy, scalability, THz/OWC/RIS integration), and outlines future directions, emphasizing the mutual reinforcement between AI development and SAGIN deployment. Collectively, the survey provides a roadmap for researchers and practitioners to harness AI for robust, scalable, and low-latency SAGIN-enabled 6G systems. The identified themes stress real-time learning, distributed computation, secure and privacy-preserving AI, and cross-layer orchestration as central to realizing practical SAGIN-AI ecosystems.

Abstract

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.

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

TL;DR

The paper systematically surveys how AI techniques, notably reinforcement learning and deep learning, are applied across SAGINs—satellites, HAPS, UAVs, and terrestrial networks—to tackle beam management, routing, resource allocation, mobility, and security in 6G. It highlights how SAGINs also enable AI advances, through edge intelligence, federated learning, and distributed data collection, while enabling new AI-focused techniques such as analog over-the-air computation and RIS-assisted optimization. The work synthesizes cross-layer AI solutions, identifies open issues (security, privacy, scalability, THz/OWC/RIS integration), and outlines future directions, emphasizing the mutual reinforcement between AI development and SAGIN deployment. Collectively, the survey provides a roadmap for researchers and practitioners to harness AI for robust, scalable, and low-latency SAGIN-enabled 6G systems. The identified themes stress real-time learning, distributed computation, secure and privacy-preserving AI, and cross-layer orchestration as central to realizing practical SAGIN-AI ecosystems.

Abstract

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.
Paper Structure (65 sections, 11 figures, 13 tables)

This paper contains 65 sections, 11 figures, 13 tables.

Table of Contents

  1. Introduction
  2. Previous Surveys
  3. Contributions and Organization
  4. An Overview of Artificial Intelligence
  5. Classification by Methodology
  6. Machine Learning
  7. Deep Learning
  8. Classification by Learning Type
  9. Supervised Learning
  10. Unsupervised Learning
  11. Reinforcement Learning
  12. AI for Satellite Communications
  13. Beam Hopping
  14. Anti-jamming
  15. Traffic Forecasting
  16. ...and 50 more sections

Figures (11)

  • Figure 1: Various components of SAGIN (satellites, HAPS, UAVs, and the terrestrial network) serving users in urban, remote, rural, and isolated areas.
  • Figure 2: Survey organization and main sections.
  • Figure 3: Different AI techniques and algorithms surveyed in this paper and their respective classification.
  • Figure 4: Challenges in satellite communications addressed by AI and discussed in this survey.
  • Figure 5: Challenges in HAPS communications addressed by AI and discussed in this survey.
  • ...and 6 more figures