Multi-Sized AI Models Assisted Low-Altitude UAV Delivery With RAN Optimization
Zhou, Longyu Leng, Supeng Li, Zonghang Liang, Tianhao Quek, Tony Q.S.
Zhou, Longyu
Leng, Supeng
Li, Zonghang
Liang, Tianhao
Quek, Tony Q.S.
Supervisor
Department
Machine Learning
Embargo End Date
Type
Journal article
Date
2026
License
Language
English
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Research Projects
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Abstract
With the development of Artificial Intelligence (AI) 1technology and wireless communication technology, Uncrewed Aerial Vehicles (UAVs)-based low-altitude applications have become increasingly attractive for accurate and real-time UAV delivery by performing cooperative path planning. However, this application exposes imperative computing requirements on resource-limited UAVs. To address the problem, we propose a multi-sized AI model-assisted parcel delivery framework with RAN resource optimization. In this design, we propose a double-scale AI model cooperation algorithm to implement highly accurate UAV path planning for safe parcel delivery. We then propose a transformer-based resource optimization algorithm to perform high-efficiency resource allocation for real-time and reliable communication and computing cooperation among UAVs. Simulation results demonstrate that our algorithm improves network throughput by 15.4% compared to other benchmarks on average while achieving a high successful delivery ratio of 92% for robust UAV delivery.
Citation
L. Zhou, S. Leng, Z. Li, T. Liang and T. Q. S. Quek, "Multi-Sized AI Models Assisted Low-Altitude UAV Delivery With RAN Optimization," in IEEE Internet of Things Magazine, doi: 10.1109/MIOT.2025.3644407
Source
IEEE Internet of Things Magazine
Conference
Keywords
Low-altitude network, RAN, reliable resource optimization, UAV swarm
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Publisher
IEEE