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。卫星凭借其广泛的覆盖范围、稳定的轨道、可扩展性以及遵守国际法规，成为NTN的主要使能载体。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和资源分配等。NTN在6G中与现有地面网络的集成带来了一系列新挑战，包括任务卸载、网络路由、网络切片等。为应对所有这些障碍，本文提出人工智能（AI）作为有前景的解决方案，利用其捕获不同网络参数之间复杂相关性的能力。我们首先提供NTN和AI的综合背景，强调AI技术在解决各种NTN挑战中的潜力。接着，我们概述现有工作，突出AI作为基于卫星NTN的使能工具，并探讨潜在研究方向。此外，我们讨论旨在通过软件定义实现来赋能基于卫星NTN中AI的持续研究努力，并探讨相关挑战。最后，我们总结并提出在未来的6G网络中实现AI驱动的基于卫星NTN的见解和建议。