Agentic AI Networking for Heterogeneous Unmanned Aerial Systems in Low-Altitude Wireless Networks

A new arXiv paper (2609.19538v1) proposes a hierarchical hybrid large language model (LLM) and multi-agent reinforcement learning (MARL) architecture for low-altitude wireless networks (LAWNs) supporting heterogeneous unmanned aerial systems. The dual-loop design uses an outer LLM-assisted game orchestration loop to interpret service requirements and operator intent and reconfigure objectives and resource priorities, while an inner loop runs decentralized, parameter-conditioned MARL policies, allowing adaptation to evolving conditions without retraining the underlying MARL policies. A logistics-monitoring case study illustrates coordinated coexistence among heterogeneous services, and the authors outline challenges toward scalable, trustworthy, and adaptive agentic LAWNs.

arXiv:2609.19538v1 Announce Type: new 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.