用生成式AI打造能感知决策的智能卫星网络，提升低空经济通信与安全
Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approaches
- 基于生成模型构建可自主感知决策的智能代理系统
- 五类生成模型在通信增强、隐私保护等场景表现各异
- 适合关注空天地一体化网络与AI Agent的科研与工程人员
卫星增强的低空经济与地面网络（SLAETNs）的发展需要能在异构、动态、任务关键环境中可靠运行的智能自主系统。本文聚焦通过生成式AI（GAI）和大语言模型（LLMs）实现智能体人工智能（AI），即具备感知、推理与行动能力的人工智能体。首先介绍SLAETN的架构与特性，分析卫星、空中与地面组件集成中的挑战。随后系统回顾五类主流生成模型：变分自编码器（VAEs）、生成对抗网络（GANs）、生成扩散模型（GDMs）、基于变压器的模型（TBMs）和大语言模型（LLMs），并进行对比分析，揭示其生成机制、能力及在SLAETNs中的部署权衡。在此基础上，探讨这些模型如何赋能通信增强、安全与隐私保护、智能卫星任务三大领域的智能体功能。最后，提出构建可扩展、自适应、可信生成智能体的关键未来方向。本综述旨在为下一代融合网络中智能体AI的发展提供统一理解与实践参考。
原文摘要 · Abstract (English)
The development of satellite-augmented low-altitude economy and terrestrial networks (SLAETNs) demands intelligent and autonomous systems that can operate reliably across heterogeneous, dynamic, and mission-critical environments. To address these challenges, this survey focuses on enabling agentic artificial intelligence (AI), that is, artificial agents capable of perceiving, reasoning, and acting, through generative AI (GAI) and large language models (LLMs). We begin by introducing the architecture and characteristics of SLAETNs, and analyzing the challenges that arise in integrating satellite, aerial, and terrestrial components. Then, we present a model-driven foundation by systematically reviewing five major categories of generative models: variational autoencoders (VAEs), generative adversarial networks (GANs), generative diffusion models (GDMs), transformer-based models (TBMs), and LLMs. Moreover, we provide a comparative analysis to highlight their generative mechanisms, capabilities, and deployment trade-offs within SLAETNs. Building on this foundation, we examine how these models empower agentic functions across three domains: communication enhancement, security and privacy protection, and intelligent satellite tasks. Finally, we outline key future directions for building scalable, adaptive, and trustworthy generative agents in SLAETNs. This survey aims to provide a unified understanding and actionable reference for advancing agentic AI in next-generation integrated networks.
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