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R-NeRF: Neural Radiance Fields for Modeling RIS-enabled Wireless Environments

Huiying Yang, Zihan Jin, Chenhao Wu, Rujing Xiong, Robert Caiming Qiu, Zenan Ling

TL;DR

This method utilizes NeRF-based ray tracing to intuitively capture and visualize the complex dynamics of signal propagation, effectively modeling the complete signal pathways from the transmitter to the RIS, and from the RIS to the receiver.

Abstract

Recently, ray tracing has gained renewed interest with the advent of Reflective Intelligent Surfaces (RIS) technology, a key enabler of 6G wireless communications due to its capability of intelligent manipulation of electromagnetic waves. However, accurately modeling RIS-enabled wireless environments poses significant challenges due to the complex variations caused by various environmental factors and the mobility of RISs. In this paper, we propose a novel modeling approach using Neural Radiance Fields (NeRF) to characterize the dynamics of electromagnetic fields in such environments. Our method utilizes NeRF-based ray tracing to intuitively capture and visualize the complex dynamics of signal propagation, effectively modeling the complete signal pathways from the transmitter to the RIS, and from the RIS to the receiver. This two-stage process accurately characterizes multiple complex transmission paths, enhancing our understanding of signal behavior in real-world scenarios. Our approach predicts the signal field for any specified RIS placement and receiver location, facilitating efficient RIS deployment. Experimental evaluations using both simulated and real-world data validate the significant benefits of our methodology.

R-NeRF: Neural Radiance Fields for Modeling RIS-enabled Wireless Environments

TL;DR

This method utilizes NeRF-based ray tracing to intuitively capture and visualize the complex dynamics of signal propagation, effectively modeling the complete signal pathways from the transmitter to the RIS, and from the RIS to the receiver.

Abstract

Recently, ray tracing has gained renewed interest with the advent of Reflective Intelligent Surfaces (RIS) technology, a key enabler of 6G wireless communications due to its capability of intelligent manipulation of electromagnetic waves. However, accurately modeling RIS-enabled wireless environments poses significant challenges due to the complex variations caused by various environmental factors and the mobility of RISs. In this paper, we propose a novel modeling approach using Neural Radiance Fields (NeRF) to characterize the dynamics of electromagnetic fields in such environments. Our method utilizes NeRF-based ray tracing to intuitively capture and visualize the complex dynamics of signal propagation, effectively modeling the complete signal pathways from the transmitter to the RIS, and from the RIS to the receiver. This two-stage process accurately characterizes multiple complex transmission paths, enhancing our understanding of signal behavior in real-world scenarios. Our approach predicts the signal field for any specified RIS placement and receiver location, facilitating efficient RIS deployment. Experimental evaluations using both simulated and real-world data validate the significant benefits of our methodology.
Paper Structure (19 sections, 12 equations, 6 figures, 2 tables)

This paper contains 19 sections, 12 equations, 6 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Preliminary: NeRF-based Ray Tracing
  4. R-NeRF:Two-stage modeling Method
  5. Two-stage Neural Network
  6. Neural Network Structure
  7. Model Training and Inference
  8. Training
  9. Inference
  10. Experiment
  11. Dataset Construction
  12. Simulation Dataset
  13. Measured Dataset
  14. Performance Evaluation
  15. Evaluation Metrics
  16. ...and 4 more sections

Figures (6)

  • Figure 1: Illustration of the signal emission and transmission in RIS-enabled wireless environments，including its interactions with the environment through reflection, diffraction, scattering and etc.
  • Figure 2: (a) The two-stage process of R-NeRF. Firstly, the ray emits from the TX to the RIS, represented by voxels $\mathbf{P_{v1-1}}$ to $\mathbf{P_{v1-6}}$. Each voxel along this path serves as a new transmitter, emitting the signal towards the RIS. Secondly, the signal continues its journey from the RIS to the RX, encountering voxels $\mathbf{P_{v2-1}}$ to $\mathbf{P_{v2-6}}$. At each voxel, the signal is changed by the intervening voxels, influencing its signal amplitude and phase upon reaching the destination. (b) Two-stage Neural network structure. We respectively use $\mathbf{F_{\Theta_1}}$ and $\mathbf{F_{\Theta_2}}$ to learn the signal characteristics of the TX-RIS stage and the RIS-RX stage, and employ rendering equations to obtain the total signal $R_{total}$.
  • Figure 3: (a) The simulation scenario, (b) The experimental scenario.
  • Figure 4: Visualization results of the ground-truth signal field (Top) v.s. R-NeRF predicted signal filed (Bottom) w.r.t different RIS positions: $\mathbf{P_{RIS-1}}$ (-0.063, 0, 0.8259), $\mathbf{P_{RIS-2}}$ (0.4703, 0, -0.063), $\mathbf{P_{RIS-3}}$ (0.8259, 0, 0.8259).
  • Figure 5: The CDF of the signal strength errors.
  • ...and 1 more figures