Aug 2026· IEEE Journal on Selected Topics in Signal Processing· pp. 1-32· 0 citations
EngineeringComputer Science
TL;DR
This tutorial presents a comprehensive treatment of LAWNs from the joint perspectives of artificial intelligence (AI) and signal processing, and highlights opportunities for LAWNs to evolve into dependable, AI-native infrastructure for the intelligent skies.
Abstract
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.
The rapid growth of the low-altitude economy calls for integrated sensing and communication (ISAC) networks capable of robust flight monitoring. This article advocates wireless imaging as a unifying sensing paradigm that enables ISAC networks to function as comprehensive low-altitude guardians. We present a hierarchical imaging framework that enhances sensing capability from wide-area snapshot imaging to dynamic trajectory-aware imaging and target-centric fine-grained characterization. At the wide-area level, low-altitude sensing is reformulated as a spatial imaging problem, where distributed base stations and communication users collaboratively construct a holistic view of the aerial space. Building on this foundation, multi-frame dynamic imaging exploits temporal correlations to support robust trajectory tracking and prediction under mobility-induced challenges such as occlusions. For security-critical scenarios, the framework further enables fine-grained imaging of flying and hovering uncrewed aerial vehicles, providing detailed characterization beyond conventional point-target abstractions. Additionally, we propose a novel evaluation metric named imaging coverage to examine the sensing fidelity of the proposed framework. Illustrative case studies demonstrate the potential of imaging in ISAC networks to support wide-area monitoring, motion-aware tracking, and fine-grained target analysis.
Yi-Xuan Huang, Jie Yang, Chao-Kai Wen et al.· IEEE Communications Magazine· 0 citations
The rapid expansion of the low-altitude economy (LAE)demands highly reliable Unmanned Aerial Vehicle (UAV) systems. However, traditional UAV control engineering suffers from severe time latency inherent in its serial architecture and is significantly amplified in complex urban environments. To address this challenge, 6G-enabled Integrated Sensing, Communication, and Computing (ISCC) architectures are being utilised to reduce transmission delays. Furthermore, Large Language Models (LLMs) are introduced to shift the design paradigm of control engineering, aiming to improve decision timeliness through predictive state estimation and reasoning. This article reviews the advantages of coupling 6G ISCC with LLMs. The synthesis indicates that this integration effectively minimises system latency by substituting iterative mathematical calculations with direct semantic prediction, its transition to real-world deployment faces severe engineering bottlenecks where the intrinsic "black-box" hallucinations of LLMs are paramount. This paper concludes that autonomous UAV operations necessitate a fundamental shift toward Trustworthy and Explainable AI (XAI), coupled with high-fidelity digital twin validation, to guarantee system stability in safety-critical domains.
Kai-Yi Chen· Applied and Computational En...· 0 citations
The highly dynamic and safety-critical characteristics of low-altitude airspace render sensing an indispensable component of low-altitude wireless networks (LAWN). Although sensing techniques have been extensively studied under diverse paradigms, a prominent mismatch persists between state-of-the-art sensing schemes and the practical sensing demands of LAWN. To fill this research gap, this article systematically reviews LAWN-oriented sensing from the dimensions of system framework, core technologies, and research trends. Specifically, we first analyze the sensing system framework, covering concepts, services and tasks, nodes and targets, and scenarios for LAWN sensing. Next, we conduct a comparative analysis of existing sensing techniques from the perspectives of propagation medium, cooperation, methodology, and modality, analyzing their advantages and limitations. Then, we summarize promising future research directions for deployable LAWN sensing systems, covering non-cooperative and cooperative sensing, model-driven and data-driven sensing, and model-and-data-driven multi-modal sensing. Finally, we present a case study of a model-and-data-driven multi-modal method for real-time aerial target sensing. Compared with existing surveys on LAWN or sensing, this article delivers a more comprehensive, targeted review exclusively centered on LAWN sensing.
Zihao Tao, Yimin Zhao, Hong-Tao Zhao et al.· 0 citations
This paper studies the fast and high-performance FA reconfiguration for low-altitude FA networks with multi-agent reinforcement learning (MARL) and presents an electromagnetic digital twin (EM-DT)-assisted MARL framework to fill the sim-to-real gap.
Tong Zhang, Yanan Su, Shuai Wang et al.· 0 citations
The rapid growth in the use of unmanned aerial vehicles (UAVs) in commercial and military applications, along with the increasing accessibility of these technologies, has created new challenges for critical infrastructure security, airspace protection, and public safety. In this context, the development of effective methods for detecting UAVs has become crucial for security and defense applications. This paper proposes an artificial intelligence-based framework for UAV detection using radio spectrum sensing methods. The training and evaluation pipeline for the AI model uses a custom dataset consisting of 36,000 RF spectrograms in the time-frequency domain. The dataset was divided into two classes: drone, which includes RF signals from 6 commercial UAVs acquired in different operating modes, and no drone, which includes signals acquired in indoor and outdoor environments. The system has been tested in real-world dynamic scenarios at various distances in congested wireless environments characterized by high RF traffic. The experimental results achieved an accuracy of over 94% in real-world operating scenarios. The obtained results show a high level of performance, highlighting the system’s potential for real-world applications in live RF monitoring and UAV sensing.
Alexandrin Gutu, A. Lavric, Valentin Popa et al.· IEEE Access· 0 citations
Low-altitude wireless networks (LAWNs) are expected to play a pivotal role in sixth-generation (6G) systems by enabling flexible, on-demand, and infrastructure-light connectivity for communication, sensing, and security-critical applications. However, the highly dynamic propagation conditions, frequent blockages, and evolving security threats inherent to LAWNs pose fundamental challenges to conventional static network architectures. In this article, we investigate flying reconfigurable intelligent surfaces (FRIS) as a transformative enabler for cell-free integrated sensing and communication (CF-ISAC) in LAWNs. By jointly exploiting aerial mobility and programmable electromagnetic reflections, FRIS introduces a new degree of freedom for three-dimensional and environment-aware control of wireless propagation. We present a comprehensive overview of enabling technologies, highlight physical layer security benefits, and provide a representative case study demonstrating the performance gains and sensing-communication trade-offs of FRIS-assisted CF-ISAC systems. Finally, we discuss key research challenges and future directions toward practical and secure 6G low-altitude deployments.
Shanza Shakoor, Quang Nhat Le, Minh-Hien T. Nguyen et al.· IEEE Communications Magazine· 0 citations
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