用开放无线网络构建智能低空经济系统，实现无人机群实时协同导航
Next Generation Intelligent Low-Altitude Economy Deployments: The O-RAN Perspective
- 基于O-RAN架构解耦无线网络，通过AI控制器实现动态任务调度
- 结合语义理解与强化学习，使无人机群在复杂地形中实时规划路径
- 适合研究低空智能交通、应急通信系统的学者和工程师
尽管低空经济（LAE）应用如无人机物流和应急响应日益受到关注，但在复杂、信号受限的环境中实现对空中节点的实时、鲁棒且情境感知的编排仍面临根本性挑战。现有方案普遍缺乏为LAE任务专门优化的人工智能（AI）集成。本文提出一种基于开放无线接入网（O-RAN）的LAE框架，利用解耦的RAN架构、开放接口与无线接入网智能控制器（RICs），实现闭环、AI优化且任务关键的LAE运行。我们通过一个语义感知的rApp作为地形解释器，为强化学习驱动的xApp提供语义指导，后者完成对LAE无人机群的实时轨迹规划。文中还调研了可用于LAE研究的无人机测试平台，提出了关键研究挑战与标准化需求。
原文摘要 · Abstract (English)
Despite the growing interest in low-altitude economy (LAE) applications, including UAV-based logistics and emergency response, fundamental challenges remain in orchestrating such missions over complex, signal-constrained environments. These include the absence of real-time, resilient, and context-aware orchestration of aerial nodes with limited integration of artificial intelligence (AI) specialized for LAE missions. This paper introduces an open radio access network (O-RAN)-enabled LAE framework that leverages seamless coordination between the disaggregated RAN architecture, open interfaces, and RAN intelligent controllers (RICs) to facilitate closed-loop, AI-optimized, and mission-critical LAE operations. We evaluate the feasibility and performance of the proposed architecture via a semantic-aware rApp that acts as a terrain interpreter, offering semantic guidance to a reinforcement learning-enabled xApp, which performs real-time trajectory planning for LAE swarm nodes. We survey the capabilities of UAV testbeds that can be leveraged for LAE research, and present critical research challenges and standardization needs.
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