2026年3月，IEEE Transactions on Network Science and Engineering（TNSE）发布主题为“AI驱动低空智能网络：从建模走向真实部署”（AI-Driven Low-Altitude Intelligent Networks: From Modeling to Real-World Deployment）的专题征稿通知。本专题由我室吴启晖教授、朱秋明教授担任客座编委，面向全球征集具有学术引领性与实践价值的创新成果，助力低空智能网络从理论建模走向真实部署，欢迎广大学者积极投稿。
As a key infrastructure for smart cities, intelligent transportation and emergency communications, Low-Altitude Intelligent Networks (LAIN) feature 3D coverage, high dynamics, time-varying topologies and intermittent connectivity. Artificial intelligence (AI) provides core driving force for LAIN modeling, optimization and deployment, enabling closed-loop perception, learning, and decision-making in dynamic environments.
This special issue welcomes advanced research results in terms of AI solutions for LAINs. Evidence of real testbeds and demos, such as figures, videos, and datasets, is strongly preferred. The scope of the special issue includes, but not limited to the following topics:
Standardization, testbeds, real-world deployment, and practical applications of LAINs
Theory-to-practice transition methodologies via AI-integrated digital twins for LAINs
Intelligent electromagnetic environment awareness and modeling for LAINs
AI-driven spectrum and interference management strategies in dynamic LAINs
Joint spectrum, energy, and computing resource optimization for practical LAINs
Sustainable distributed cloud-edge-end collaborative deployment for LAINs
AI-native LAIN architectures for integrated sensing, communication, and computing
On-site learning and adaptive decision-making for real-time LAINs
Agentic-AI-enabled autonomous network control and self-optimization in practical LAINs
Learning-enabled topology dynamics for multiple aerial platforms in LAINs
Embodied intelligence for perception-decision-action under environmental uncertainty in LAINs
Uncertainty-aware network optimization under practical unreliable state information
Fail-operational and graceful-degradation network design for safety-critical LAINs
AI-driven adaptive self-healing mechanisms for real-world LAINs
Edge AI and lightweight on-device learning for link adaptation under fast-varying air-to-ground channels in LAINs
Privacy and data security protocol design in AI-based LAINs
Prospective authors are invited to submit their manuscripts electronically, adhering to the IEEE Transactions on Network Science and Engineering guidelines. Note that the page limit is the same as that of regular papers. Please submit your papers through the online system, and be sure to select the special issue or special section name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts, to the ScholarOne portal. If requested, abstracts should be sent by email directly to the Guest Editors.
Qiuming Zhu, Nanjing University of Aeronautics and Astronautics, China
Mahbub Hassan, University of New South Wales, Australia
Marion Berbineau, Université Gustave Eiffel, France
Carlos A. Gutierrez, Universidad Autonoma de San Luis Potosi, Mexico
Qihui Wu, Nanjing University of Aeronautics and Astronautics, China
Manuscript Submission: 1 August 2026
First Review Round: 1 October 2026
Revision Papers Due: 15 November 2026
Acceptance Notification: 31 December 2026
Final Manuscript Due: 31 January 2027
Publication Date: First Quarter 2027
IEEE TNSE is a leading forum for research at the intersection of network science and engineering, emphasizing modeling, system-level analysis, and practical deployment. This special issue aims to fill this gap by focusing on AI-driven LAINs that involve modeling, learning, optimization, and real-world deployment, which aligns closely with the journal’s mission. By encouraging contributions that integrate network science, AI technology, and system engineering, the special issue aims to advance both theoretical foundations and practical deployment for LAINs.
https://www.comsoc.org/publications/journals/ieee-tnse/cfp/ai-driven-low-altitude-intelligent-networks-modeling-real-world