用于射频光线追踪的神经反射率场
Neural Reflectance Fields for Radio-Frequency Ray Tracing
AI总结：
本文提出可微 RF 光线追踪框架与神经反射率场，利用信号路径损耗学习各入射角的材料反射系数，以更少训练数据提升接收机功率预测精度。
摘要：
光线追踪被广泛用于对复杂环境中的射频（RF）信号传播进行建模。建模性能在很大程度上取决于能够多准确地描述目标场景，包括场景几何结构和表面材料属性。计算机视觉和 LiDAR 的进展使场景几何估计日益精确，但仍缺乏可扩展且高效的方法来估计真实环境中的材料反射率。在本文中，我们通过从发射机到接收机的 RF 信号路径损耗中高效学习材料反射率来解决这一问题。具体而言，我们希望所学的材料反射系数能够最小化接收机预测功率与测量功率之间的差距。为此，我们将神经反射率场从光学领域迁移到 RF 领域，通过同时建模 RF 信号的振幅和相位来解释多径效应。我们进一步提出了一个可微 RF 光线追踪框架，用于优化神经反射率场以匹配信号强度测量。我们模拟了一个复杂的真实环境进行实验，仿真结果表明，神经反射率场能够成功学习所有入射角的反射系数。因此，与现有方法相比，我们的方法使用显著更少的训练数据，在预测接收机功率方面实现了更高的精度。
英文摘要：
Ray tracing is widely employed to model the propagation of radio-frequency (RF) signal in complex environment. The modelling performance greatly depends on how accurately the target scene can be depicted, including the scene geometry and surface material properties. The advances in computer vision and LiDAR make scene geometry estimation increasingly accurate, but there still lacks scalable and efficient approaches to estimate the material reflectivity in real-world environment. In this work, we tackle this problem by learning the material reflectivity efficiently from the path loss of the RF signal from the transmitters to receivers. Specifically, we want the learned material reflection coefficients to minimize the gap between the predicted and measured powers of the receivers. We achieve this by translating the neural reflectance field from optics to RF domain by modelling both the amplitude and phase of RF signals to account for the multipath effects. We further propose a differentiable RF ray tracing framework that optimizes the neural reflectance field to match the signal strength measurements. We simulate a complex real-world environment for experiments and our simulation results show that the neural reflectance field can successfully learn the reflection coefficients for all incident angles. As a result, our approach achieves better accuracy in predicting the powers of receivers with significantly less training data compared to existing approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
