发表机构
La Trobe University; Technical University of Berlin; University of New South Wales（拉筹伯大学; 柏林工业大学; 新南威尔士大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对低空无线网络中异构无人机系统的动态非合作博弈问题，提出一种LLM-MARL双环架构，通过外层LLM编排博弈目标与内层参数化MARL执行，实现无需重训练的自适应协调共存。
AI 中文摘要
低空无线网络（LAWNs）正成为支撑异构无人机系统在共享三维空域内并发服务的关键基础设施。这些系统的共存导致移动性、连通性和共享网络资源之间产生强耦合，而异构服务则提出不同且随时间变化的需求。这些交互自然形成一个动态非合作博弈，其中运行条件和协调目标均随时间演变。传统优化和基于学习的控制器通常依赖预定义目标，限制了其自主适应不断变化的服务需求和资源优先级的能力。为应对这一挑战，我们提出一种分层混合大语言模型（LLM）-多智能体强化学习（MARL）架构，组织为双环结构。具体而言，外层适应环采用LLM辅助的博弈编排来解读服务需求和操作员意图，并重新配置目标和资源优先级，而内层则在配置的博弈下执行去中心化的、参数条件的MARL策略。一个物流监控案例研究展示了所提框架如何促进异构服务间的协调共存，适应不断演变的运行条件而无需重新训练底层MARL策略。最后，我们讨论了实现可扩展、可信赖且自适应的智能体LAWNs的关键挑战和研究方向。
英文摘要
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.
