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RayProNet: A Neural Point Field Framework for Radio Propagation Modeling in 3D Environments

Ge Cao, Zhen Peng

arXiv:2406.16907v1eess.SPcs.LG

TL;DR

Ray tracing complexity grows with ray-object interactions, and new transmitter/receiver configurations typically require another simulation. RayProNet uses point-cloud geometry, light probes, and spherical-harmonic field encoding to predict propagation for new transmitter/receiver locations and antenna patterns. The proposed method runs 80 ∼200 times faster than GPU-accelerated ray tracing methods and predicts wireless channel properties across small indoor, medium outdoor, and large city scenes.

  • Problem

    Ray tracing complexity grows with ray-object interactions, and new transmitter/receiver configurations typically require another simulation.

    Source: §I · p. 1

  • Method

    RayProNet uses point-cloud geometry, light probes, and spherical-harmonic field encoding to predict propagation for new transmitter/receiver locations and antenna patterns.

    Source: §I · pp. 1–2, §III · p. 3, C. Path Tracing with Light Probes and… · p. 4

  • Results

    The proposed method runs 80 ∼200 times faster than GPU-accelerated ray tracing methods and predicts wireless channel properties across small indoor, medium outdoor, and large city scenes.

    Source: §I · p. 2, §V · p. 9

  • Takeaways & Limitations

    The reported speed advantage is particularly beneficial when transmitter and receiver locations change frequently during wireless deployment and planning.

    Source: §V · p. 9

    Geometry and occlusion information are embedded in the neural networks, and the authors identify changes to scene geometry as a limitation.

    Source: §V · p. 9

Abstract

from arXiv · show

The radio wave propagation channel is central to the performance of wireless communication systems. In this paper, we introduce a novel machine learning-empowered methodology for wireless channel modeling. The key ingredients include a point-cloud-based neural network and a Spherical Harmonics encoder with light probes. Our approach offers several significant advantages, including the flexibility to adjust antenna radiation patterns and transmitter/receiver locations, the capability to predict radio power maps, and the scalability of large-scale wireless scenes. As a result, it lays the groundwork for an end-to-end pipeline for network planning and deployment optimization. The proposed work is validated in various outdoor and indoor radio environments.

I. INTRODUCTION

Ray tracing provides channel information but becomes costly as ray-object interactions grow, and location changes can require new simulations. RayProNet uses point-cloud scenes and light probes to model propagation, with reported scalability and flexibility across indoor and outdoor scenes.

I. INTRODUCTION

  • Ray tracing complexity grows with ray-object interactions, and changing transmitter/receiver locations typically requires another simulation.These demands create a gap between simulation time and the rapid time-to-solution needed for wireless network design and optimization.Source: §I · p. 1
  • The neural surrogate is trained on ray-tracing solutions for finite transmitter locations, then predicts propagation for new transmitter/receiver locations and antenna patterns.It also predicts spatial radiated-power distributions, including radio coverage or path-loss maps, and is intended to generalize to large indoor and outdoor scenes.Source: §I · p. 1
  • The framework represents 3D wireless scenes with point clouds and embeds channel-state information in light probes, using a Spherical Harmonics encoder and decoder to query propagation by ray direction.The probes are designed to capture site-specific electromagnetic propagation physics.Source: §I · pp. 1–2
  • RayProNet is reported to scale across small indoor, medium outdoor, and large city scenes while maintaining high-quality channel prediction and flexibility across geometry complexity.The paper also positions it against neural ray-tracing methods on scalability and flexibility.Source: §I · p. 2

II. RELATED WORKS

  • Related path-loss surrogates often use 2D bird’s-eye-view heatmaps without explicit geometry [19]–; mesh triangles are limited in deep learning by non-differentiability, and attempted differentiability strategies remain computationally intensive.The network draws inspiration from NeuralPointLF and adds relighting for changing antenna locations or radiation patterns.Source: §II · p. 2

III. NEURAL POINT FIELD FOR WIRELESS CHANNEL

RayProNet models ray tracing between transmitters and receivers in complex wireless scenes using point-cloud geometry, light probes, and spherical-harmonic field extraction.

III. NEURAL POINT FIELD FOR WIRELESS CHANNEL

  • RayProNet is a neural point-field network intended to simulate ray tracing between transmitters and receivers in complex wireless scenes.The pipeline combines point-cloud geometry, light probes for propagation information, and spherical-harmonic functions for field extraction.Source: §III · p. 3
  • Point clouds represent the scene geometry, while light probes capture path-tracing and ray-propagation information.These are two of the framework’s three stated technical elements.Source: §III · p. 3
  • Spherical-harmonic functions extract field data from the neural point-field pipeline.The paper identifies this as its third fundamental element.Source: §III · p. 3

A. Data Preparation

The data-preparation stage represents environment geometry as point clouds and places light probes throughout the scene to capture propagation behavior. PointNet then embeds normalized scene point clouds into features for later interpolation.

A. Data Preparation

  • RayProNet takes transmitter and receiver locations plus 3D environment geometry, which it converts to point clouds to encode obstacles, buildings, and terrain.Light probes are placed uniformly through the scene to capture ray paths, reflections, and diffractions, particularly where geometry is sparse.Source: A. Data Preparation · p. 3
  • The preparation pipeline configures transmitter and receiver locations and antenna patterns, then associates receivers with nearby light probes and probes with nearby scene points.The numbers of nearest probes and points, n and K, are adjustable hyperparameters for scene complexity and application needs.Source: A. Data Preparation · p. 3

B. Point Cloud Feature Embedding

  • PointNet processes scene point clouds directly after normalization to [−1, 1], producing a feature matrix with 128 features per point cloud.The matrix is split into two 64-dimensional feature matrices for subsequent interpolation.Source: B. Point Cloud Feature Embedding · p. 3

C. Path Tracing with Light Probes and Point Clouds

Light probes capture propagation information in sparse scenes where rays may miss nearby geometry. Point-cloud features, transmitter information, and direction queries are combined to encode this information for receiver queries.

C. Path Tracing with Light Probes and Point Clouds

D. Receivers: Unveiling Ray Physics from Light Probes

Receivers query nearby light probes with ray directions and attention to extract EM power. The resulting ray features include a separate receiver–transmitter line-of-sight ray.

D. Receivers: Unveiling Ray Physics from Light Probes

E. Spherical Harmonics-based Decoding of Ray Features

Ray features are decoded using spherical harmonics to represent directional information. An MLP produces interpolation coefficients, which are split before the spherical harmonics decoder is applied.

E. Spherical Harmonics-based Decoding of Ray Features

IV. NUMERICAL EXPERIMENTS

The experiments evaluate wireless signal strength and coverage prediction across multiple scene scales, using ray-tracing data for training and validation. The setup combines an isolated-building physics test with scene-specific data and a fixed training procedure.

A. Experimental Setup and Training

  • The evaluation targets path loss maps and received signal strength at designated locations, outcomes used in network planning and optimization.These predictions inform antenna placement and transmission-power decisions.Source: A. Experimental Setup and Training · p. 5, §IV · p. 5
  • The isolated-building test compares the model’s EM-propagation predictions with ray-tracing ground truth.The figure describes the framework as capturing EM-physics principles in a scene with one building at the center.Source: Figure 7 · p. 6

1) Data Collection:

  • The datasets use wiindoor, etoile, and Munich scenes, spanning a small indoor room, medium city block, and large urban city.Data are generated with the Sionna ray-tracing simulator.Source: 1) Data Collection · p. 6, Table I · p. 6
  • Each scene has approximately 175–375 transmitter locations and 1,920–35,816 uniformly sampled receiver locations; 85% are used for training and 15% for validation at 2.14 GHz.The trained model is used as a neural surrogate for wireless channel prediction.Source: 1) Data Collection · p. 6

2) Training setups:

  • Training uses n = 8 nearest light probes, K = 8 attached point clouds, batch size 1000, and learning rate 0.0001 for 500 epochs.Training uses Adam and MSE loss for received-power optimization, taking 1.5–10 hours on an NVIDIA GeForce RTX 3090 Ti.Source: 2) Training setups · p. 6

3) Evaluation Metric:

The evaluation uses MSE and PSNR to assess path-loss-map predictions, tests learning of EM propagation physics with an isolated building, and compares RayProNet with neural surrogates. RayProNet is reported to closely match ray-tracing ground truth and outperform the compared surrogates.

3) Evaluation Metric:

  • MSE and PSNR are the evaluation metrics for comparing predicted radio path-loss maps with ground truth or measurements.MSE and PSNR are also used in the reported comparison of power predictions.Source: 3) Evaluation Metric · p. 6, Table II · p. 7

1) Assessment in Learning EM Propagation Physics:

  • With an isolated building, validation tests whether the network captures EM-propagation features using training data with and without diffraction effects.The passage reports that refining the training dataset can improve prediction accuracy.Source: 1) Assessment in Learning EM… · p. 6, Figure 8 · p. 7

2) Comparison to Other Neural Surrogates:

3) Verification through Ablation Experiment:

The ablation removes light probes and has receivers search directly for nearby point clouds instead. The reported analysis links missed nearby buildings to wasted rays and limits on power prediction in both outdoor and indoor scenes.

3) Verification through Ablation Experiment:

  • When rays miss nearby buildings, they are wasted as default latent features; the ablation analysis reports significantly limited power prediction in the outdoor and indoor scenes.Without light probes, receivers directly search for the K closest point clouds; the ablation compares MSE and PSNR on etoile and wiindoor.Source: 3) Verification through Ablation… · p. 7, Table III · p. 7, Figure 9 · p. 7

C. Evaluation in Large-scale, 3D Wireless Scenes

RayProNet is evaluated across indoor, outdoor, and urban scene scales, and with different antenna radiation patterns. The reported predictions agree with ray-tracing results across these settings.

1) Large-scale Environment:

  • Predictions across small indoor, medium outdoor, and large urban scenes are reported as consistent with ray-tracing results.The small-room test also reports accurate capture of complex ray trajectories; Fig. 10 presents the range of scene scales.Source: 1) Large-scale Environment · p. 8, Figure 10 · p. 8
  • The runtime comparison uses 25 transmitters, four antenna patterns, and 148×121 receivers, or 100×100 receivers for the small indoor scene.Table IV compares model and ray-tracing runtime; the supplied text gives no runtime outcome.Source: Table IV · p. 8, 1) Large-scale Environment · p. 8

2) Antenna Radiation Pattern:

3) Quantitative Measurements:

The section evaluates 3D power prediction at 15 receiver points and reports 80–200× faster modeling than GPU-accelerated ray tracing. It also identifies scene-geometry changes as a limitation because they require retraining.

3) Quantitative Measurements:

V. CONCLUSION

  • The paper describes this as the first effort in 3D neural wireless channel modeling that handles large-scale input scenes.It contrasts this scope with prior 2D image approaches and WINERT’s focus on small indoor scenes, whose mesh-triangle one-hot mapping has excessive memory requirements for large scenes.Source: §V · p. 9
  • 80–200 times faster than GPU-accelerated ray tracing methods for modeling extensive 3D scenes.The paper notes this efficiency is beneficial when transmitter and receiver locations frequently change.Source: §V · p. 9
  • Geometry and occlusion are embedded in the framework, so scene-geometry changes necessitate pipeline retraining.The paper identifies adapting to geometry changes without retraining as future work.Source: §V · p. 9
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