单体素无线NeRF用于空间频谱预测
Single-Voxel Wireless NeRF for Spatial Spectrum Prediction
- Rensselaer Polytechnic Institute（伦斯勒理工学院）
- IBM
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出SV-INGP，一种稀疏体素采样的无线NeRF变体，用于高效预测空间频谱幅度，在匹配NeRF2基线SSIM的同时将训练时间减少184倍，为射频数字孪生提供更简化的设计路径。
AI中文摘要：
跨多个空间方向的无线信道测量对于AI驱动的应用（如射频数字孪生和集成通信与感知）至关重要。然而，在大场景中收集信道数据是劳动密集型的。无线NeRF通过从稀疏测量中学习传播行为并在未见位置合成信道空间频谱幅度来解决这一挑战。然而，现有的无线NeRF继承了视觉NeRF的密集体素采样，这需要大量计算。本文探讨了这种密集采样对于预测无线空间频谱幅度是否必要。我们实证表明，无线NeRF在此任务上过度参数化，并引入了SV-INGP，即即时神经图形基元（INGP）的稀疏体素采样变体。在真实和模拟数据集上，SV-INGP匹配了NeRF2基线的中位结构相似性指数度量（SSIM），同时将训练时间减少了184倍。这些结果在视距（LoS）和非视距（NLoS）场景、sub-6和毫米波频率以及天线阵列锥削配置中普遍适用，表明射频数字孪生存在更简单、更高效的设计路径。
英文摘要：
Wireless channel measurements across multiple spatial directions are crucial for AI-driven applications, such as RF digital twins and integrated communication and sensing. However, collecting channel data across large scenes is labor-intensive. Wireless NeRFs address this challenge by learning propagation behavior from sparse measurements and synthesizing channel spatial spectrum magnitude at unseen locations. However, existing wireless NeRFs inherit dense volumetric sampling from vision NeRFs, which requires substantial computation. This paper asks whether such dense sampling is necessary for predicting magnitudes of the wireless spatial spectrum. We empirically show that wireless NeRFs are over-parameterized for this task and introduce SV-INGP, a sparse volumetric sampling variant of Instant Neural Graphics Primitives (INGP). Across real-world and simulated datasets, SV-INGP matches the median Structural Similarity Index Measure (SSIM) of the NeRF2 baseline while reducing training time by 184x. These results generalize across LoS and NLoS scenes, sub-6 and millimeter-wave frequencies, and antenna array tapering configurations, suggesting a simpler and more efficient design path for RF digital twins
