Toward Realization of Low-Altitude Economy Networks: Core Architecture, Integrated Technologies, and Future Directions

TL;DR

The paper addresses the challenge of realizing scalable, safe, and intelligent low-altitude operations by proposing a core LAE network architecture and detailing the integrated technologies that enable such networks. It surveys enabling technologies across communications, sensing, computing, positioning/navigation/surveillance, and flight-control/airspace management, and demonstrates how multi-technology integration supports applications in logistics, rescue, and transportation. Core contributions include articulating the LAE architectural layers, aligning with relevant standards, and outlining future directions—intelligent optimization, security/privacy, sustainable energy, quantum coordination, generative governance, and 3D airspace coverage—that collectively chart a path toward robust LAE networks. The work underscores the practical impact by outlining how collaborative technologies can improve operational efficiency, airspace utilization, and safety in urban and regional environments, enabling scalable UAV/eVTOL-enabled services.

Abstract

The rise of the low-altitude economy (LAE) is propelling urban development and emerging industries by integrating advanced technologies to enhance efficiency, safety, and sustainability in low-altitude operations. The widespread adoption of unmanned aerial vehicles (UAVs) and electric vertical takeoff and landing (eVTOL) aircraft plays a crucial role in enabling key applications within LAE, such as urban logistics, emergency rescue, and aerial mobility. However, unlike traditional UAV networks, LAE networks encounter increased airspace management demands due to dense flying nodes and potential interference with ground communication systems. In addition, there are heightened and extended security risks in real-time operations, particularly the vulnerability of low-altitude aircraft to cyberattacks from ground-based threats. To address these, this paper first explores related standards and core architecture that support the development of LAE networks. Subsequently, we highlight the integration of technologies such as communication, sensing, computing, positioning, navigation, surveillance, flight control, and airspace management. This synergy of multi-technology drives the advancement of real-world LAE applications, particularly in improving operational efficiency, optimizing airspace usage, and ensuring safety. Finally, we outline future research directions for LAE networks, such as intelligent and adaptive optimization, security and privacy protection, sustainable energy and power management, quantum-driven coordination, generative governance, and three-dimensional (3D) airspace coverage, which collectively underscore the potential of collaborative technologies to advance LAE networks.

Figures (12)

• Figure 1: The survey paper is structured as follows: Overview of LAE Networks (Section II), Enabling Technologies for LAE Network Development (Section III), Multi-Technology Integration for Low-Altitude Applications (Section IV), and Future Directions for Collaborative Technologies in LAE Networks (Section V).
• Figure 2: GAI-driven computing in LAE networks. Part A presents an MEC-based architecture, where GAI supports applications including text generation, AI chatbots, and decision-making. Part B illustrates a cloud-edge-end collaborative framework, where GAI facilitates functions such as image generation, video generation, and graph creation.
• Figure 3: Proposed multi-user wireless network architecture under the ISCC paradigm in Zhao2022. Specifically, the BS utilizes ISAC waveforms to integrate communication and sensing while collaborating with IoT devices for computational tasks. Moreover, MEC-based edge infrastructure and AI-enabled software provide intelligent decision support.
• Figure 4: An integrated UAV navigation and positioning framework. Part A presents the GNSS module, which utilizes BDS, Galileo, GPS, and GLONASS for initial positioning, with A-GNSS and ground stations enhancing accuracy. Part B outlines the SLAM process, where the front end handles feature extraction and data association, while the back end performs map estimation and pose optimization. Part C depicts the INS module, which leverages accelerometer and gyroscope data for attitude and navigation calculations to determine position, velocity, and orientation.
• Figure 5: Overall structure of the INS/LiDAR SLAM LC integration system in s22124327. Specifically, the IMU module processes acceleration and angular velocity data to compute the inertial navigation solution. Moreover, the LiDAR SLAM module extracts position and attitude information from LiDAR point clouds, while the GNSS/INS module provides a GNSS-aided inertial navigation solution as an additional reference. Finally, the EKF filter fuses these solutions, updates errors, and generates the final navigation solution. Furthermore, the feedback mechanism corrects IMU mechanization errors through a closed-loop compensation scheme, thereby enhancing the robustness and accuracy of the system.