TY - JOUR
T1 - Toward Realization of Low-Altitude Economy Networks
T2 - Core Architecture, Integrated Technologies, and Future Directions
AU - Wang, Yixian
AU - Sun, Geng
AU - Sun, Zemin
AU - Wang, Jiacheng
AU - Li, Jiahui
AU - Zhao, Changyuan
AU - Wu, Jing
AU - Liang, Shuang
AU - Yin, Minghao
AU - Wang, Pengfei
AU - Niyato, Dusit
AU - Sun, Sumei
AU - In Kim, Dong
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2025
Y1 - 2025
N2 - 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 uncrewed 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. We also analyze how generative artificial intelligence (GAI) contributes to this integration by enabling intelligent adaptation, uncertainty modeling, and real-time optimization. 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.
AB - 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 uncrewed 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. We also analyze how generative artificial intelligence (GAI) contributes to this integration by enabling intelligent adaptation, uncertainty modeling, and real-time optimization. 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.
KW - Low-altitude economy (LAE) networks
KW - electric vertical takeoff and landing (eVTOL)
KW - generative artificial intelligence (GAI)
KW - multi-technology integration
KW - uncrewed aerial vehicles (UAVs)
UR - https://www.scopus.com/pages/publications/105013875929
U2 - 10.1109/TCCN.2025.3601015
DO - 10.1109/TCCN.2025.3601015
M3 - Article
AN - SCOPUS:105013875929
SN - 2332-7731
VL - 11
SP - 2788
EP - 2820
JO - IEEE Transactions on Cognitive Communications and Networking
JF - IEEE Transactions on Cognitive Communications and Networking
IS - 5
ER -