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Diffusion^2: Turning 3D Environments into Radio Frequency Heatmaps

Kyoungjun Park, Yifan Yang, Changhan Ge, Lili Qiu, Shiqi Jiang

TL;DR

Diffusion^2 presents a diffusion-based framework that converts 3D indoor environment models into RF heatmaps across Wi‑Fi and mmWave bands by conditioning a diffusion process on rich RF-3D features. It introduces RF-3D Encoder and RF-3D Pairing Block to fuse 3D geometry, 2D views, and RF properties, enabling both static heatmaps and dynamic RF heatmap videos. The approach achieves state-of-the-art accuracy (around 1.9 dB for Wi‑Fi and 1.20 dB for mmWave) with substantially faster inference (up to 27× faster than baselines) and requires relatively few measurements, drastically reducing data collection overhead. By supporting multiple frequencies and dynamic scenes, Diffusion^2 offers a practical tool for wireless planning, diagnosis, and optimization in complex environments.

Abstract

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.

Diffusion^2: Turning 3D Environments into Radio Frequency Heatmaps

TL;DR

Diffusion^2 presents a diffusion-based framework that converts 3D indoor environment models into RF heatmaps across Wi‑Fi and mmWave bands by conditioning a diffusion process on rich RF-3D features. It introduces RF-3D Encoder and RF-3D Pairing Block to fuse 3D geometry, 2D views, and RF properties, enabling both static heatmaps and dynamic RF heatmap videos. The approach achieves state-of-the-art accuracy (around 1.9 dB for Wi‑Fi and 1.20 dB for mmWave) with substantially faster inference (up to 27× faster than baselines) and requires relatively few measurements, drastically reducing data collection overhead. By supporting multiple frequencies and dynamic scenes, Diffusion^2 offers a practical tool for wireless planning, diagnosis, and optimization in complex environments.

Abstract

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.

Paper Structure

This paper contains 35 sections, 18 equations, 14 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Motivation:
  3. Related work
  4. Design of Diffusion$^2$
  5. Overview
  6. Condition-guided denoising process.
  7. RF-3D Pairing Block
  8. RF-3D Encoder
  9. 3D feature.
  10. 2D feature.
  11. RF signal feature.
  12. Unified condition representation.
  13. Network training
  14. Generating RF heatmap video
  15. Evaluation
  16. ...and 20 more sections

Figures (14)

  • Figure 1: Results for Diffusion$^2$, AutoMSautoms, NeRF$^2$zhao2023nerf, and MRI shin2014mri for one example environment at two frequencies. The transmitter is located in the upper right corner of the room. 5.16 GHz and 77 GHz are used for Wi-Fi and millimeter wave (mmWave). Diffusion$^2$ and NeRF$^2$ are tested using the same 15 pre-measurements.
  • Figure 2: Overview of the diffusion process in Diffusion$^2$. RF-3D features condition the denoising process, while different modalities are fused through the RF-3D Pairing Block.
  • Figure 3: RF-3D Encoder embedding 2D, 3D, and RF signal
  • Figure 4: Wi-Fi signal prediction
  • Figure 5: mmWave signal prediction
  • ...and 9 more figures