Agentic AI coordinates complex drone networks in shared airspace
Agentic AI Networking for Heterogeneous Unmanned Aerial Systems in Low-Altitude Wireless Networks
Artificial IntelligenceMultiagent Systems
Summary
Managing many different types of drones flying together in the same airspace is hard because they move, connect, and share resources all at once. The authors created a system where a smart language model helps set goals and priorities, while multiple AI agents control the drones based on those goals. This lets the whole network adjust smoothly when needs change, without starting over. They tested their system on a delivery monitoring example to show how it works.
What this means in practice
- •For drone fleet operators: Coordinate diverse drone missions in shared 3D airspace while adapting to changing service priorities without retraining AI policies.
- •For wireless network engineers: Manage competing connectivity and resource demands in dense low-altitude wireless networks using hierarchical AI controllers.
Authors
Nguyen Duc Minh Quang, Chang Liu, Shuangyang Li, Derrick Wing Kwan Ng
Abstract
Low-altitude wireless networks (LAWNs) are emerging as a key infrastructure for heterogeneous unmanned aerial systems that support concurrent services within a shared three-dimensional airspace. Their coexistence creates strong coupling among mobility, connectivity, and shared network resources, while heterogeneous services impose distinct and time-varying requirements. These interactions naturally form a dynamic non-cooperative game in which both operating conditions and coordination objectives evolve over time. Conventional optimization and learning-based controllers typically rely on predefined objectives, limiting their ability to adapt autonomously to changing service requirements and resource priorities. To address this challenge, we propose a hierarchical hybrid large language model (LLM)- multi-agent reinforcement learning (MARL) architecture organized as a dual-loop structure. Specifically, an outer adaptation loop employs LLM-assisted game orchestration to interpret service requirements and operator intent, and reconfigure objectives and resource priorities, while an inner loop executes decentralized, parameter-conditioned MARL policies under the configured game. A logistics-monitoring case study illustrates how the proposed framework facilitates coordinated coexistence among heterogeneous services, adapting to evolving operating conditions without retraining the underlying MARL policies. Finally, we discuss key challenges and research directions toward scalable, trustworthy, and adaptive agentic LAWNs.