用AI解决低空经济网络的频谱与移动性难题，推动从仿真到实际部署。
AI-Driven Low-Altitude Economy: Spectrum, Mobility, and Validation
- 基于AI的分布式频谱感知与共存机制，提升资源利用效率。
- 强化学习驱动联合资源分配与轨迹优化，适应高动态环境。
- 通过真实飞行平台验证模型，适合研究低空智能网络的学者与工程师。
低空经济（LAE）网络凭借其变革性能力，有望成为未来十年空中交通的重要技术突破。然而，预期的空前密度、高移动性和异构性带来了挑战，传统基于规则的方法已不再适用。为此，本研究提出基于人工智能（AI）的方法与验证框架，推动AI技术从仿真研究向实际可部署系统转化。研究讨论了智能LAE网络的关键使能技术：首先，引入利用LAE节点分布特性的AI辅助频谱感知与共存机制；其次，探讨了由强化学习驱动的联合资源分配与轨迹优化；最后，通过航空实验与先进无线研究平台（AERPAW）等实验平台，弥合仿真与部署之间的差距，实现真实非平稳空域条件下的模型验证。研究还指出了开放问题，并提出了高效、互操作且可扩展的AI驱动LAE生态系统的前瞻性发展路线图。
原文摘要 · Abstract (English)
The Low Altitude Economy (LAE) network, with its transformative capabilities, is a candidate to become one of the major technological developments of the next decade for air mobility. However, the expected unprecedented density, mobility, and heterogeneity pose challenges and require new approaches, as it renders traditional rule-based approaches inadequate. To address these challenges, this study introduces artificial intelligence (AI)-based approaches and validation frameworks for transitioning AI-enabled technologies from simulation-based studies to practical and deployable systems. This study discusses essential enablers for intelligent LAE networks. First, AI-based spectrum sensing and coexistence utilizing the distributed nature of LAE nodes is introduced. Then, joint resource allocation and trajectory optimization driven by reinforcement learning is discussed. Bridging the gap between simulation and deployment through experimental platforms such as Aerial Experiments and Research Platform for Advanced Wireless (AERPAW), which are critical for validating models under realistic and non-stationary airspace conditions, is also addressed. The study concludes by highlighting open issues and outlining a forward-looking roadmap for the development of efficient, interoperable, and scalable AI-driven LAE ecosystems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。