标题： 原生人工智能赋能的未来非地面网络可扩展架构与解决方案：综述 显示英文标题

标题： Native-AI Empowered Scalable Architectures and Solutions for Future Non-Terrestrial Networks: An Overview

摘要： 随着6G网络的路径逐渐明确，新兴的应用推动了网络架构的演进，以实现高效、可靠和灵活的无线网络。 在潜在的架构中，非地面网络（NTN）和开放无线接入网络（ORAN）受到了学术界和工业界的越来越多关注。 尽管NTN的部署确保了覆盖范围，提高了频谱效率，并增强了无线网络的弹性。 NTN的高海拔和移动性在开发和运营（DevOps）生命周期中带来了新的挑战，由于缺乏原生的人工智能（AI）能力，阻碍了智能和可扩展的网络管理。 鉴于ORAN在解耦、开放性、虚拟化和智能化方面的优势，一些研究提出将ORAN原则整合到NTN中，主要集中在基于透明和再生系统的ORAN部署选项。 然而，关于如何在整个DevOps生命周期中有效结合ORAN和NTN的全面视角仍然缺失，尤其是在智能ORAN如何解决NTN中的可扩展性挑战方面。 受此驱动，本文首先提供关于ORAN和NTN的背景知识，概述关于ORAN用于NTNs的最新研究，并介绍促使采用ORAN解决方案的DevOps挑战。 然后，我们提出了基于ORAN的NTN框架，详细讨论其特性与架构。 这些包括关于灵活前传分割的讨论、针对分布式学习的RAN智能控制器（RICs）增强、可扩展的部署架构以及多域服务管理。 最后，强调了未来的研究方向，包括基于ORAN的NTN框架与其他使能技术及方案的结合，以及候选的应用场景。 显示英文摘要

摘要： As the path toward 6G networks is being charted, the emerging applications have motivated evolutions of network architectures to realize the efficient, reliable, and flexible wireless networks. Among the potential architectures, the non-terrestrial network (NTN) and open radio access network (ORAN) have received increasing interest from both academia and industry. Although the deployment of NTNs ensures coverage, enhances spectral efficiency, and improves the resilience of wireless networks. The high altitude and mobility of NTN present new challenges in the development and operations (DevOps) lifecycle, hindering intelligent and scalable network management due to the lack of native artificial intelligence (AI) capability. With the advantages of ORAN in disaggregation, openness, virtualization, and intelligence, several works propose integrating ORAN principles into the NTN, focusing mainly on ORAN deployment options based on transparent and regenerative systems. However, a holistic view of how to effectively combine ORAN and NTN throughout the DevOps lifecycle is still missing, especially regarding how intelligent ORAN addresses the scalability challenges in NTN. Motivated by this, in this paper, we first provide the background knowledge about ORAN and NTN, outline the state-of-the-art research on ORAN for NTNs, and present the DevOps challenges that motivate the adoption of ORAN solutions. We then propose the ORAN-based NTN framework, discussing its features and architectures in detail. These include the discussion about flexible fronthaul split, RAN intelligent controllers (RICs) enhancement for distributed learning, scalable deployment architecture, and multi-domain service management. Finally, the future research directions, including combinations of the ORAN-based NTN framework and other enabling technologies and schemes, as well as the candidate use cases, are highlighted.