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

arXiv: https://arxiv.org/abs/2609.19538 · HTML: https://mlsanity.com/paper/2609.19538/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agentic AI for wireless networks, one of 16 directions in LLM agents and tool use.
- Builds on 17 of its 22 references that are in the map.
- First posted on arXiv 17 Sep 2026.

## Placed in

- LLM agents and tool use / [Agentic AI for wireless networks](https://mlsanity.com/agents/agentic-ai-for-wireless-networks/)

## Builds on (most cited first, 17 of 17)

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- [Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial](https://mlsanity.com/paper/2011.03615/) (2020)
- [When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment](https://mlsanity.com/paper/2401.07764/) (2024)
- [Toward Edge General Intelligence with Agentic AI and Agentification: Concepts, Technologies, and Future Directions](https://mlsanity.com/paper/2508.18725/) (2025)
- [Deep Residual Learning for Channel Estimation in Intelligent Reflecting Surface-Assisted Multi-User Communications](https://mlsanity.com/paper/2009.01423/) (2020)
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- [Learning-based Predictive Beamforming for Integrated Sensing and Communication in Vehicular Networks](https://mlsanity.com/paper/2108.11540/) (2021)
- [Toward Realization of Low-Altitude Economy Networks: Core Architecture, Integrated Technologies, and Future Directions](https://mlsanity.com/paper/2504.21583/) (2025)
- [From Agentification to Self-Evolving Agentic AI for Wireless Networks: Concepts, Approaches, and Future Research Directions](https://mlsanity.com/paper/2510.05596/) (2025)
- [Low-Altitude Wireless Networks: A Comprehensive Survey](https://mlsanity.com/paper/2509.11607/) (2025)
- [Toward Low-Altitude Airspace Management and UAV Operations: Requirements, Architecture and Enabling Technologies](https://mlsanity.com/paper/2506.08579/) (2025)
- [Predictive Precoder Design for OTFS-Enabled URLLC: A Deep Learning Approach](https://mlsanity.com/paper/2212.13651/) (2022)
- [Diffusion Model-Enhanced Environment Reconstruction in ISAC](https://mlsanity.com/paper/2511.19044/) (2025)
- [Toward Multi-Functional LAWNs with ISAC: Opportunities, Challenges, and the Road Ahead](https://mlsanity.com/paper/2508.17354/) (2025)
- [3D Dynamic Radio Map Prediction Using Vision Transformers for Low-Altitude Wireless Networks](https://mlsanity.com/paper/2511.19019/) (2025)
- [Game-Theoretic Multi-Agent Reinforcement Learning for Swarm Trajectory Planning in Low-Altitude Wireless Networks](https://mlsanity.com/paper/2606.16386/) (2026)

## Abstract (arXiv)

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.