AI 中文总结
本教程从AI与信号处理视角，全面阐述低空无线网络（LAWN）的架构基础、信号处理与AI技术，结合案例分析其融合应用，指出未来发展挑战与机遇。
AI 中文摘要
由无人驾驶飞行器（UAV）推动的低空航空服务与应用快速发展，催生了超越传统地面网络的新型数字基础设施需求。低空无线网络（LAWN）被提出作为可动态重构的三维架构，集成空中与地面节点，在开放、安全关键的空域提供连通性、感知与控制服务。本教程从人工智能（AI）与信号处理相结合的视角，全面阐述LAWN相关内容：首先回顾LAWN的历史演进与架构基础，介绍基于高度的分层结构与功能平面，并总结监管与标准化现状；在此系统视角基础上，讨论LAWN的信号处理基础，包括三维信道与系统模型、性能指标、波形与接收机设计、定位与跟踪，以及多功能协同设计；接着调研适用于LAWN的AI技术，涵盖用于感知、控制、资源管理与安全的判别式与生成式模型，以及用于任务规划与闭环优化的基础模型、大语言模型、数字孪生等新兴范式；为说明AI与信号处理在实践中的融合，提供了一个由AI驱动的、包含混合卫星、高空与地面节点的多层LAWN案例研究；最后，本教程概述了LAWN在架构设计、信号处理-AI协同设计、安全与安保、实验验证及标准化方面的关键研究挑战，并强调LAWN有望演进为面向智能天空的可靠、原生AI基础设施的机遇。
英文摘要
The rapid growth of low-altitude aerial services and applications, driven by uncrewed aerial vehicles (UAVs), calls for a new class of digital infrastructure beyond conventional terrestrial networks. The low-altitude wireless network (LAWN) has been proposed as dynamically reconfigurable three-dimensional architectures that integrate aerial and ground nodes to provide connectivity, sensing, and control in open, safety-critical airspace. This tutorial presents a comprehensive treatment of LAWNs from the joint perspectives of artificial intelligence (AI) and signal processing. We first review the historical evolution and architectural foundations of LAWNs, introducing altitude-based layers and functional planes, and summarizing the regulatory and standardization landscape. Building on this system view, we then discuss signal processing fundamentals for LAWNs, including 3D channel and system models, performance metrics, waveform and receiver design, localization and tracking, and multi-functionality co-design. Next, we survey AI techniques for LAWNs, covering discriminative and generative models for perception, control, resource management, and security, as well as emerging paradigms such as foundation models, large language models, and digital twins for mission planning and closed-loop optimization. To illustrate AI-signal processing integration in practice, we provide a case study of an AI-driven multi-tier LAWN with hybrid satellite, high-altitude, and ground nodes. The tutorial concludes by outlining key research challenges in architecture design, signal processing-AI co-design, safety and security, experimentation, and standardization, and by highlighting opportunities for LAWNs to evolve into dependable, AI-native infrastructure for the intelligent skies.
CommentsInvited Overview Paper in JSTSP
