用3D点云和扩散模型预测复杂环境中的无线信号分布
Diffusion^2: Turning 3D Environments into Radio Frequency Heatmaps
- 基于3D点云构建射频特征编码器，模拟信号传播过程
- 在多频段环境中误差仅1.9 dB，速度比现有方法快27倍
- 适合无线网络优化、信号诊断等实际场景应用
建模射频（RF）信号传播对理解环境至关重要，因为射频信号能提供远超可见光相机的能力，克服光谱限制、镜头视野和遮挡问题。这对无线诊断、部署与优化具有重要意义。然而，在复杂环境中准确预测射频信号仍面临挑战，因障碍物引起的吸收与反射效应复杂。本文提出Diffusion^2，一种基于扩散模型的方法，利用3D点云在从Wi-Fi到毫米波的广泛频率范围内建模射频信号传播。为有效捕捉3D数据中的射频特征，我们设计了RF-3D Encoder，融合三维几何结构与信号特异性信息，并通过多尺度嵌入模拟真实的射频传播过程。基于合成与真实测量数据的评估显示，Diffusion^2在多种频率与环境条件下均能精准估计信号行为，误差仅为1.9 dB，且速度比现有方法快27倍，显著推进该领域进展。
原文摘要 · Abstract (English)
Modeling radio frequency (RF) signal propagation is essential for understanding the environment, as RF signals offer valuable insights beyond the capabilities of RGB cameras, which are limited by the visible-light spectrum, lens coverage, and occlusions. It is also useful for supporting wireless diagnosis, deployment, and optimization. However, accurately predicting RF signals in complex environments remains a challenge due to interactions with obstacles such as absorption and reflection. We introduce Diffusion^2, a diffusion-based approach that uses 3D point clouds to model the propagation of RF signals across a wide range of frequencies, from Wi-Fi to millimeter waves. To effectively capture RF-related features from 3D data, we present the RF-3D Encoder, which encapsulates the complexities of 3D geometry along with signal-specific details. These features undergo multi-scale embedding to simulate the actual RF signal dissemination process. Our evaluation, based on synthetic and real-world measurements, demonstrates that Diffusion^2 accurately estimates the behavior of RF signals in various frequency bands and environmental conditions, with an error margin of just 1.9 dB and 27x faster than existing methods, marking a significant advancement in the field. Refer to https://rfvision-project.github.io/ for more information.
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