摘要：低空无线网络（LAWNs）正成为异构无人航空系统的重要基础设施，这些系统可在共享的三维空域内同时提供多种服务。它们的共存使移动性、连接性和共享网络资源之间形成强耦合，而异构服务则提出了不同且随时间变化的需求。这些交互自然形成了一种动态非合作博弈，其中运行条件和协调目标都会随时间演变。传统优化方法和基于学习的控制器通常依赖预先定义的目标，因此难以自主适应不断变化的服务需求和资源优先级。为应对这一挑战，我们提出了一种分层混合式大语言模型（LLM）-多智能体强化学习（MARL）架构，并将其组织为双循环结构。具体而言，外层适应循环利用 LLM 辅助的博弈编排来解读服务需求和运营方意图，并重新配置目标与资源优先级；内层循环则在已配置的博弈条件下执行去中心化、参数条件化的 MARL 策略。一个物流监测案例研究说明，该框架如何促进异构服务之间的协调共存，并在无需重新训练底层 MARL 策略的情况下适应不断变化的运行条件。最后，本文讨论了实现可扩展、可信且自适应的智能体式低空无线网络所面临的关键挑战和研究方向。

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.