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<p>The growing demand for global digital connectivity has intensified efforts to integrate Terrestrial Networks (TN) and Non-Terrestrial Networks (NTN) to extend coverage to remote and underserved areas. However, unlike TN, NTN introduces unique technical and regulatory challenges, including propagation delays, Doppler shifts, spectrum authorization, and orbital licensing. As a result, seamless integration requires innovative solutions addressing both technical and regulatory domains. Artificial intelligence (AI) offers powerful tools to enhance resource allocation, mobility management, spectrum coexistence, and service continuity across integrated TN and NTN systems; however, AI deployment must also consider governance, auditability, and compliance requirements. This paper explores the integration of TN and NTN technologies, with a particular focus on the role of AI in addressing these challenges. Unlike conventional rule-based or model-driven approaches, AI offers adaptive and data-driven methods that enhance network performance through improved resource allocation, traffic forecasting, and more. The paper reviews key AI-driven strategies, ongoing standardization efforts, and emerging architectural frameworks. By synthesizing recent advancements, including developments highlighted in the 3<sup>rd</sup> Generation Partnership Project (3GPP) Releases, the article outlines future directions for building robust and efficient TN and NTN integration to support the evolution of 5G and the transition to 6G.</p>

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Integrated terrestrial and non-terrestrial networks via artificial intelligence: concept, opportunities, and challenges

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月3日

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  • Muhammad Ali Jamshed,
  • Bushra Haq,
  • Malik Muhammad Saad,
  • Muhammad Ahmed Mohsin,
  • Xingqin Lin

摘要

The growing demand for global digital connectivity has intensified efforts to integrate Terrestrial Networks (TN) and Non-Terrestrial Networks (NTN) to extend coverage to remote and underserved areas. However, unlike TN, NTN introduces unique technical and regulatory challenges, including propagation delays, Doppler shifts, spectrum authorization, and orbital licensing. As a result, seamless integration requires innovative solutions addressing both technical and regulatory domains. Artificial intelligence (AI) offers powerful tools to enhance resource allocation, mobility management, spectrum coexistence, and service continuity across integrated TN and NTN systems; however, AI deployment must also consider governance, auditability, and compliance requirements. This paper explores the integration of TN and NTN technologies, with a particular focus on the role of AI in addressing these challenges. Unlike conventional rule-based or model-driven approaches, AI offers adaptive and data-driven methods that enhance network performance through improved resource allocation, traffic forecasting, and more. The paper reviews key AI-driven strategies, ongoing standardization efforts, and emerging architectural frameworks. By synthesizing recent advancements, including developments highlighted in the 3rd Generation Partnership Project (3GPP) Releases, the article outlines future directions for building robust and efficient TN and NTN integration to support the evolution of 5G and the transition to 6G.