Artificial Intelligence-Driven Integration of Terrestrial and Non-Terrestrial Networks: A Perspective

As sixth-generation (6G) mobile communication accelerates its evolution towards the vision of a satellite-terrestrial integrated network, the deep integration of terrestrial networks (TNs) and non-terrestrial networks (NTNs) has become an inevitable trend for achieving seamless, ubiquitous, and ultra-reliable global communication services. From the perspective of artificial intelligence (AI) technology development, this paper systematically elucidates the application potential of key technologies such as machine learning (ML), deep learning (DL), and large language models (LLMs) in satellite communication networks. This paper first outlines the evolutionary trend of AI technology and analyzes its unique advantages in handling challenges such as highly dynamic topologies, resource constraints, and massive access in low Earth orbit (LEO) satellite networks. Then, from the three dimensions of the physical layer, the network layer, and the service layer, it delves into the application of AI in core scenarios such as channel estimation, anti-interference, transmission optimization, network topology design, intelligent routing, beam resource management, and satellite edge computing. Finally, addressing key challenges such as large-scale node deployment, highly dynamic topology changes, and wide-area seamless coverage, it proposes a systematic solution based on game theory, deep learning, network slicing, and network simulation, and looks forward to future research directions for AI-driven satellite-terrestrial integrated networks, providing important references for building a fully covered, low-latency, highly reliable, and intelligent next-generation mobile communication architecture.