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

Yixian Wang, Geng Sun, Zemin Sun, Jiacheng Wang, Jiahui Li, Changyuan Zhao, Jing Wu, Shuang Liang, Minghao Yin, Pengfei Wang, Dusit Niyato, Sumei Sun, Dong In Kim

arXiv:2504.21583v1cs.NI

TL;DR

LAE networks must address dense-airspace coordination, scalability, and safety demands that challenge traditional UAV systems. This survey examines standards, layered architecture, and integrated technologies across communication, sensing, computing, navigation, control, and airspace management, concluding with collaborative research directions while noting unresolved robustness needs.

  • Problem

    Traditional UAV networks face scalability, coordination, communication, and safety limitations in dense and complex low-altitude environments.

  • Method

    The survey analyzes LAE standards and architecture, then synthesizes integrated communication, sensing, computing, positioning, navigation, surveillance, flight control, and airspace management technologies.

  • Results

    56.93% and 48.86% positioning-accuracy improvements over MLAT and the expected TDoA technique, respectively, are reported for ATBAS.

  • Takeaways & Limitations

    Collaborative technologies support efficient, safe, scalable, and sustainable LAE applications including logistics, rescue, transportation, and aerial mobility.

Abstract

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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.

I. INTRODUCTION

LAE networks address urban, logistics, and emergency-response challenges by combining underused low-altitude airspace with advanced aircraft and coordinated technologies. Their development requires intelligent decision-making, precise collaboration, elastic scheduling, and dynamic airspace management.

  • Traditional UAV systems face regulatory, technological, and scalability barriers in dense, resource-constrained, cross-regional environments.
  • LAE uses underutilized airspace between 500 and 3000 meters while integrating dynamic management and layered operations.
  • eVTOL, hydrogen propulsion, distributed thrust, shared airspace, and reusable infrastructure support runway-independent, resource-efficient operations.
  • Communication, sensing, and computing enable real-time environmental awareness and adaptive flight decisions, including risk avoidance and path optimization.
  • High-precision collaboration combines multimodal sensing, anti-jamming navigation, and real-time monitoring to support centimeter-level positioning and millisecond-level responses.
  • Elastic scheduling uses dynamic data analysis and digital twins to respond to logistics demand and weather changes while improving airspace and energy efficiency.
  • Dynamic airspace allocation and conflict prediction are needed to coordinate heterogeneous aircraft while remaining compatible with international management standards.
  • The survey focuses on multidimensional synergy among supporting technologies and the challenges affecting sustainable LAE development.

A. Related Works and Contributions

The survey distinguishes itself by examining LAE’s core architecture and applications through integrated communication, sensing, computing, positioning, navigation, control, and airspace-management technologies. It synthesizes existing work and identifies future collaborative directions.

  • Prior surveys established foundations for LAE technologies and applications, while this survey emphasizes its distinct contributions.
  • Existing studies address LAIT architectures, lifecycle management, 3D coverage, integrated sensing and communication, beamforming, and embodied intelligence.
  • The survey focuses specifically on LAE network architecture and applications rather than general surveys and tutorials.
  • It examines how integrated technologies support efficient and secure operations, aircraft coordination, and airspace management in complex scenarios.
  • The paper highlights LAE’s distinctive features and presents the core architecture supporting practical implementation and continued advancement.
  • It analyzes deep integration across communication, sensing, computing, positioning, navigation, surveillance, flight control, and airspace management.
  • The survey outlines future directions spanning adaptive optimization, security, sustainable power, quantum coordination, generative governance, and 3D coverage.
  • The paper is organized around LAE visions and architecture, enabling technologies, integrated applications, future directions, and conclusions.

A. Visions toward LAE Networks

LAE networks are envisioned as scalable, coordinated, autonomous, and reliable systems supported by standards and a layered architecture. They integrate aircraft, infrastructure, communication, sensing, control, and airspace functions for dense low-altitude operations.

  • A. Visions toward LAE Networks: Traditional UAV networks struggle with massive connectivity because dense operations produce interference and communication disruptions beyond a few dozen aircraft.
  • A. Visions toward LAE Networks: IEEE 1939.1-2021 addresses large-scale connectivity through communication-quality requirements, security standards, airspace structure, and route-design optimization.
  • A. Visions toward LAE Networks: Independent sensing, communication, navigation, and control create transmission delays that limit real-time performance in dynamic scenarios.
  • A. Visions toward LAE Networks: IEEE 1937.8-2024 integrates these functions through cellular terminals supporting BVLOS control, high-definition video, and flight-data handling.
  • A. Visions toward LAE Networks: AI, AGI, and GAI are proposed to help UAVs autonomously learn, plan, collaborate, and adapt beyond human-operated task management.
  • A. Visions toward LAE Networks: P1954 supports self-organizing, spectrum-flexible UAV communication by incorporating air-to-air communication and dynamic spectrum coexistence mechanisms.
  • B. Architecture of LAE Networks: The architecture is designed for stability, scalability, and collaboration among airborne terminals, infrastructure, and operational components.
  • B. Architecture of LAE Networks: The airborne terminal layer supports aircraft power, flight control, environmental sensing, and information exchange through integrated core systems.

C. Lessons Learned

Integrated technologies support large-scale UAV operations, coordination, autonomy, and critical tasks across LAE applications. However, robustness and redundancy in interference and congestion remain insufficiently validated.

  • LAE networks show potential for large-scale UAV operations, seamless coordination, AI-driven autonomy, and reliable critical tasks.
  • Layered architecture integrates airborne terminals, infrastructure, intelligent collaboration, digital airspace management, and multiparty coordination for scalable operations.
  • Performance under signal interference and airspace congestion still requires validation because robustness and redundancy mechanisms remain inadequate.

III. ENABLING TECHNOLOGIES FOR LAE NETWORK DEVELOPMENT

LAE networks rely on three core technology groupings: integrated communication, sensing, and intelligent computing; collaborative positioning, navigation, and surveillance; and fused flight control and airspace management.

  • LAE network development is organized around three core technologies: communication-sensing-computing integration, positioning-navigation-surveillance collaboration, and flight-control-airspace-management fusion.

A. Integration of Communication, Sensing, and Intelligent Computing

Communication, sensing, and intelligent computing jointly support real-time transmission, environmental perception, and adaptive decision-making in LAE networks. The section reviews next-generation connectivity, integrated sensing, and GAI-driven computing while identifying interference, coordination, data-fusion, and open-air-interface challenges.

  • Communication, sensing, and intelligent computing enable real-time data transmission, environmental detection, rapid analysis, adaptive flight paths, and task execution.
  • 1) Next-Generation Communication: 5G-Advanced integrates technologies including mmWave, massive MIMO, and NOMA to address growing LAE demands for bandwidth, reliability, latency control, coverage, and capacity.mmWave provides large bandwidth and high data rates but requires precise beamforming or dense network layouts because of propagation loss and poor penetration.
  • 2) Integrated-Active and Passive Sensing: Integrated sensing combines collaborative active sensing with non-collaborative passive sensing to improve environmental perception through complementary multimodal sensors and existing wireless signals.Active radar can provide high-precision distance and velocity data, while passive sensing detects environmental changes without dedicated transmission signals.
  • Real-world LAE deployment remains challenged by interference, real-time coordination, multimodal data fusion, rapidly growing IoT access demands, and dynamic open air interfaces.
  • 3) GAI-Driven Computing: GAI supports MEC and cloud-edge-end collaboration by optimizing task offloading, resource allocation, scheduling, and prediction in dynamic LAE environments.The surveyed architectures apply GAI to computational efficiency, system coordination, and low-latency performance, including MEC-based and cloud-edge-end frameworks.
  • Table II indicates that integrated communication, sensing, and intelligent computing provide data transmission, stable connectivity, environmental sensing, and decision support, but individual technologies are insufficient alone.

B. Collaboration of Positioning, Navigation, and Surveillance

Positioning, navigation, and surveillance technologies jointly support accurate localization, robust navigation, and real-time monitoring in LAE networks. Their integration improves situational awareness and safety, while security vulnerabilities and incomplete three-way synergy remain important challenges.

  • High-Accuracy Positioning: GNSS provides accurate three-dimensional positioning, while assisted GNSS accelerates satellite acquisition and improves positioning accuracy through real-time corrections.A-GNSS transmits satellite ephemeris and related corrections through terrestrial mobile communication networks, shortening time to first fix.
  • Dynamic-Fusion Navigation: SLAM and INS support localization and navigation when GNSS signals are unstable or unavailable, while multi-sensor fusion improves precision and robustness.Fusion approaches combine visual or LiDAR sensing, inertial measurements, GNSS data, and semantic information for complex environments.
  • Dynamic-Fusion Navigation: The INS/LiDAR SLAM integration system uses IMU, LiDAR SLAM, GNSS/INS, and EKF filtering to generate and refine a final navigation solution.A closed-loop feedback mechanism corrects IMU mechanization errors, enhancing system robustness and accuracy.
  • Real-Time Surveillance: ADS-B enables real-time aircraft surveillance by broadcasting GNSS-based position, speed, heading, and altitude data to aircraft and ground stations.Its high precision, low latency, and wide coverage support monitoring and collision avoidance in complex low-altitude airspace.
  • Real-Time Surveillance: ATBAS improves positioning accuracy by 56.93% over MLAT and 48.86% over expected TDoA through TDoA fingerprint grid models trained with OpenSky data.The approach addresses the need to validate the authenticity of position information in ADS-B messages.
  • Integrated Collaboration: Existing research often studies positioning, navigation, or surveillance separately or in pairs, leaving their full synergistic integration insufficiently explored.The survey identifies deeper integration among all three technologies as a direction for improving LAE network capabilities.

C. Fusion of Flight Control with Airspace Management

The section reviews advanced flight-control methods and airspace-management models for safe, efficient low-altitude operations. It emphasizes that integrating both areas remains an underexplored priority.

  • Advanced-Flight Control: PID control stabilizes aircraft using real-time attitude and position feedback but struggles with nonlinear systems, disturbances, and parameter uncertainties.
  • Advanced-Flight Control: Hybrid PID and intelligent active force control, with iterative learning adjustment, improves quadrotor disturbance rejection and agility.
  • Advanced-Flight Control: MPC predicts future aircraft states and constraints to generate optimized control inputs, unlike PID’s focus on current errors.
  • Advanced-Flight Control: AI-based MPC combines lower-frequency baseline control from MPC with higher-frequency DRL adjustments for changing disturbances, terrain, and obstacles.
  • Efficient Airspace Management: Airspace management models include fully mixed, layered, zoning, and corridor approaches, balancing flexibility, coordination complexity, safety, and capacity.
  • Integration Gap: Existing research has not deeply explored comprehensive flight-control and airspace-management fusion, motivating joint implementation for accuracy, resource distribution, and flexibility.

IV. MULTI-TECHNOLOGY INTEGRATION FOR LOW-ALTITUDE LOGISTICS, TRAFFIC, AND RESCUE

The paper examines multi-technology integration in three real-world low-altitude applications: logistics, rescue, and transportation.

  • Multi-technology integration is applied to low-altitude logistics operations.
  • Multi-technology integration is applied to low-altitude rescue operations.
  • Multi-technology integration is applied to low-altitude transportation operations.

A. Low-Altitude Logistics

Low-altitude logistics uses complementary communication, positioning, sensing, and control technologies for long-distance transport and precise last-mile delivery.

  • Long-Distance Logistics: Long-distance UAV logistics seeks stable, efficient transport across regions, including rural, mountainous, and river-crossing areas with weak signal coverage.
  • Long-Distance Logistics: LEO satellite communication can maintain consistent coverage when 5G-A experiences interference or signal loss during remote logistics flights.
  • Last-Mile Logistics: Short-distance deliveries prioritize rapid, precise, and safe last-mile completion within compact areas.
  • Last-Mile Logistics: GNSS and 5G-A or Wi-Fi support positioning and data transmission, while SLAM improves environmental awareness for avoiding urban and residential obstacles.

B. Low-Altitude Rescue

Low-altitude rescue operations integrate reliable communications, sensing, localization, and collaborative computing to support rapid response in complex or remote environments.

  • Rescue Operations: Rescue UAVs require rapid response, precise positioning, and environmental awareness to reach targets and execute emergency tasks.
  • Rescue Operations: 5G-A and LEO satellite communications provide reliable links between UAVs and rescue command centers for continuous instructions and data transmission.
  • Rescue Operations: GAI-driven edge computing enables real-time flight-path adjustment, obstacle avoidance, and target localization, while cloud computing supports disaster mapping and task allocation.

C. Low-Altitude Transportation

Low-altitude transportation depends on coordinated communication, surveillance, flight control, and airspace management to maintain connectivity, avoid conflicts, and use crowded airspace efficiently. Multi-technology integration improves transportation efficiency, safety, and adaptability, while introducing security and management requirements.

  • Communication Support: 5G-A provides low-latency airspace updates, while LEO satellite communication maintains aircraft connectivity in remote or signal-restricted areas.Together, these links support prompt flight-path adjustments and continued contact with ground control centers.
  • Surveillance and Collision Avoidance: ADS-B supplies real-time position and speed data that helps aircraft detect intersecting trajectories and avoid possible collisions.It also provides vertical alerts for conflicts involving aircraft at the same altitude.
  • Flight Control: AI-based flight control predicts risks and calculates flight paths using real-time sensor and airspace information.The system bases path calculation on aircraft dynamics.
  • Airspace Management: ATM dynamically adjusts flight plans according to aircraft position, speed, and task requirements to coordinate activities and maintain safe separation.ATM also provides real-time tracking and scheduling in complex airspace.
  • Lessons Learned: Integrated technologies improve UAV transportation efficiency, safety, and adaptability, but complex integration, high costs, and reliability issues remain barriers.The reported benefits apply across logistics, rescue, and transportation applications.
  • Future Management: High-density, multi-type aircraft require intelligent adaptive management using distributed decision-making, digital twins, sensors, and communication devices.These components support real-time aircraft-status monitoring and intelligent traffic modeling.

C. Sustainable Power and Energy Management of Aircraft

Future LAE networks must address aircraft energy demands while coordinating increasingly large-scale operations and expanding coverage. Proposed directions combine aerial energy infrastructure, quantum optimization, self-evolving governance, and LEO-enabled 3D connectivity.

  • Sustainable Power and Energy Management of Aircraft: Aerial energy relay networks could use solar-powered UAVs as charging stations to create an air-to-air energy supply chain.This direction targets sustainable energy provision for large-scale deployment of energy-consuming eVTOLs and UAVs.
  • Quantum-Driven Coordination: Quantum annealing and variational quantum circuits could accelerate route optimization, task allocation, and conflict resolution in high-density airspace.The motivation is the exponential growth of state-action spaces and multi-agent constraints in massive-scale coordination.
  • Generative Governance: Generative governance frameworks use self-evolving cognitive agents that learn across communication, control, and navigation layers.Agents can train in large-scale simulations and adapt through real-time in situ learning to handle unknown traffic, unexpected tasks, and policy changes.
  • 3D Airspace Coverage: LEO satellite integration can provide seamless 3D coverage and reliable cross-regional communication where ground infrastructure is limited or environmental conditions are extreme.Dynamic orbits, low latency, and wide coverage support this role.
  • Conclusion: The paper identifies intelligent optimization, security and privacy, sustainable energy, quantum coordination, generative governance, and 3D coverage as future LAE-network directions.These directions are presented as applications of collaborative technologies in LAE networks.
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