Non-Terrestrial Networks (NTN) are expected to be a critical component of 6th Generation (6G) networks, providing ubiquitous, continuous, and scalable services. Satellites emerge as the primary enabler for NTN, leveraging their extensive coverage, stable orbits, scalability, and adherence to international regulations. However, satellite-based NTN presents unique challenges, including long propagation delay, high Doppler shift, frequent handovers, spectrum sharing complexities, and intricate beam and resource allocation, among others. The integration of NTNs into existing terrestrial networks in 6G introduces a range of novel challenges, including task offloading, network routing, network slicing, and many more. To tackle all these obstacles, this paper proposes Artificial Intelligence (AI) as a promising solution, harnessing its ability to capture intricate correlations among diverse network parameters. We begin by providing a comprehensive background on NTN and AI, highlighting the potential of AI techniques in addressing various NTN challenges. Next, we present an overview of existing works, emphasizing AI as an enabling tool for satellite-based NTN, and explore potential research directions. Furthermore, we discuss ongoing research efforts that aim to enable AI in satellite-based NTN through software-defined implementations, while also discussing the associated challenges. Finally, we conclude by providing insights and recommendations for enabling AI-driven satellite-based NTN in future 6G networks.

翻译：非地面网络（NTN）预计将成为第六代（6G）网络的关键组成部分，提供无处不在、连续且可扩展的服务。卫星凭借其广泛覆盖、稳定轨道、可扩展性及符合国际法规等优势，成为NTN的主要使能技术。然而，基于卫星的NTN面临独特挑战，包括长传播时延、高多普勒频移、频繁切换、频谱共享复杂性以及复杂的波束与资源分配等问题。将NTN集成至6G现有地面网络更引发了一系列新挑战，如任务卸载、网络路由、网络切片等。为应对这些挑战，本文提出人工智能（AI）作为一种前景广阔的解决方案，利用其捕获多样化网络参数间复杂关联的能力。我们首先系统阐述NTN与AI的背景知识，重点探讨AI技术在解决各类NTN挑战中的潜力。继而综述现有研究成果，强调AI作为卫星NTN使能工具的作用，并探索潜在研究方向。此外，我们讨论了通过软件定义实现方式在卫星NTN中部署AI的持续研究进展，同时剖析相关挑战。最后，本文就未来6G网络中实现AI驱动的卫星NTN提出见解与建议。