低空无线电地图构建的主动学习：基于即插即用流匹配

中文摘要 AI 辅助

无人机在低空空域的部署需要准确且及时的无线电地图，以确保可靠的通信和安全导航。然而，由于全面测量的高昂开销和无人机有限的飞行续航能力，构建此类无线电地图面临挑战。为解决这一问题，我们提出了一种基于流匹配的主动学习框架，用于从稀疏测量中高效构建低空无线电地图。我们首先分析了一种带有流匹配先验的即插即用（PnP）推理方案。通过常微分方程（ODE）刻画后期细化行为，我们从理论上展示了推理步骤如何平滑地与连续ODE流对齐以细化地图细节。鉴于早期生成阶段主要受噪声主导，这一见解促使我们提出了截断流匹配即插即用（TFM-PnP）方法。TFM-PnP利用基于空间插值的初始化，从中间流时间开始重建，从而绕过低效的早期阶段。我们进一步利用流匹配的生成多样性推导出不确定性图，以指导无人机轨迹设计。具体而言，我们提出了一种加权采样方法来选择目标位置，并采用效用感知路径搜索（UAPS）算法来设计相应的无人机轨迹。基于Sionna射线追踪数据集的仿真结果表明，所提出的框架优于所考虑的基线方法，归一化均方误差（NMSE）降低了50%以上。

英文摘要

The deployment of unmanned aerial vehicles (UAVs) in low-altitude airspace requires accurate and timely radio maps for reliable communication and safe navigation. However, constructing such radio maps is challenging due to the prohibitive overhead of exhaustive measurements and the limited flight endurance of UAVs. To address this challenge, we propose an active learning framework based on flow matching for efficient low-altitude radio map construction from sparse measurements. We first analyze a plug-and-play (PnP) inference scheme with a flow-matching prior. By characterizing the late-stage refinement behavior through an ordinary differential equation (ODE), we theoretically show how the inference steps smoothly align with a continuous ODE flow to refine the map details. Recognizing that the early generative stages are largely noise-dominated, this insight motivates our proposed truncated flow matching plug-and-play (TFM-PnP) approach. TFM-PnP utilizes a spatial interpolation-based initialization to start the reconstruction from an intermediate flow time, thereby bypassing the inefficient early stages. We further use the generative diversity of flow matching to derive an uncertainty map to guide the UAV trajectory design. Specifically, we propose a weighted sampling approach to select a target location, followed by a Utility-Aware Path Search (UAPS) algorithm to design the corresponding UAV trajectories. Simulation results based on Sionna ray-tracing datasets show that the proposed framework outperforms the considered baselines, achieving more than 50% reduction in normalized mean squared error (NMSE).

发表机构

• City University of Hong Kong（香港城市大学）
• Pennsylvania State University（宾夕法尼亚州立大学）
• City University of Hong Kong (Dongguan)（香港城市大学（东莞））

机构由 AI 辅助整理，请以论文原文为准。