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UAV Control and Communication Enabled Low-Altitude Economy: Challenges, Resilient Architecture and Co-design Strategies

Tianhao Liang, Nanchi Su, Yuqi Ping, Guangyu Lei, Xinglin Chen, Longyu Zhou, Tingting Zhang, Qinyu Zhang, Tony Q. S. Quek

Abstract

The emerging low-altitude economy has catalyzed the large-scale deployment of unmanned aerial vehicles (UAVs), driving a paradigm shift in environment monitoring, logistics, and emergency response. However, operating within these environments presents notable challenges as pervasive coverage holes, unpredictable interference, and spectrum scarcity. To this end, this article present a communication and control co-design framework to enable a resilient architecture for cellular-connected UAVs. Specifically, we first characterize typical service applications and their stringent performance requirements, followed by a comprehensive analysis of the unique challenges. To bridge the gap between volatile wireless links and rigid flight stability, a three layered architecture is proposed, integrating pre-flight strategic planning, in-flight adaptive action, and system-level resource orchestration. Furthermore, we detail the key enabling technologies for communication and control co-design. Preliminary case studies are proposed to validate that the co-design framework significantly improve the resilience of cellular-connected UAV systems, providing a robust foundation for the evolution of intelligent low-altitude networks.

UAV Control and Communication Enabled Low-Altitude Economy: Challenges, Resilient Architecture and Co-design Strategies

Abstract

The emerging low-altitude economy has catalyzed the large-scale deployment of unmanned aerial vehicles (UAVs), driving a paradigm shift in environment monitoring, logistics, and emergency response. However, operating within these environments presents notable challenges as pervasive coverage holes, unpredictable interference, and spectrum scarcity. To this end, this article present a communication and control co-design framework to enable a resilient architecture for cellular-connected UAVs. Specifically, we first characterize typical service applications and their stringent performance requirements, followed by a comprehensive analysis of the unique challenges. To bridge the gap between volatile wireless links and rigid flight stability, a three layered architecture is proposed, integrating pre-flight strategic planning, in-flight adaptive action, and system-level resource orchestration. Furthermore, we detail the key enabling technologies for communication and control co-design. Preliminary case studies are proposed to validate that the co-design framework significantly improve the resilience of cellular-connected UAV systems, providing a robust foundation for the evolution of intelligent low-altitude networks.

Paper Structure

This paper contains 19 sections, 1 equation, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Emerging Applications and Design Challenges
  3. Emergency Rescue and Response
  4. Data Collection and Monitoring
  5. Swarm Coordination
  6. A Closed-Loop Resilient Architecture
  7. Pre-flight Strategic Planning
  8. In-flight Adaptive Action
  9. System-level Resource Orchestration
  10. Key Enabling Technologies for Co-design
  11. Connectivity based Trajectory Planning
  12. Adaptive Communication and Control Co-design
  13. Control-Oriented Scheduling for UAV Swarms
  14. Case Studies: From Single-UAV Co-Design to Multi-UAV Closed-Loop Scheduling
  15. Conclusions and Future Work
  16. ...and 4 more sections

Figures (4)

  • Figure 1: The illustration of emerging applications of cellular-connected UAVs in low-altitude economy.
  • Figure 2: The illustration of our resilient communication and control framework for cellular-connected UAV systems.
  • Figure 3: Adaptive communication and control co-design for a remote UAV system. The left panel shows the target and realized trajectories in the presence of static and dynamic obstacles. The lower-left panel shows the self-triggered control length over time. The three zoomed views on the right highlight representative operating regions.
  • Figure 4: Case study of the proposed context-aware closed-loop scheduling framework for UAV swarms. (a) System architecture with a remote control node and a UAV swarm under limited bidirectional wireless links. (b) Trajectory tracking example, where the UAV follows the reference path and receives denser updates in the high-risk area. (c) Average control error versus the total number of UAVs under different numbers of UAVs that can access the channel in each time slot.