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Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling
Jakob Hoydis, Fayçal Aït Aoudia, Sebastian Cammerer, Merlin Nimier-David, Nikolaus Binder, Guillermo Marcus, Alexander Keller
TL;DR
Existing stochastic models cannot provide the spatially consistent scene-to-channel correspondence needed by many 6G radio-environment simulations. The paper presents Sionna RT, a differentiable ray-tracing extension combining Mitsuba 3 and TensorFlow, and demonstrates its use for learning radio materials and optimizing transmitter orientation. The framework integrates ray tracing, link-level simulation, and machine learning in an open-source platform intended to support applications including digital twin networks.
Problem
Many 6G applications require spatially consistent radio-environment simulation, while stochastic channel models cannot provide correspondence between physical locations and channel impulse responses.
Method
Sionna RT combines Mitsuba 3 scene and ray-intersection processing with TensorFlow-based field computation and automatic differentiation.
Results
The paper demonstrates gradient-based learning of radio material parameters and optimization of transmitter orientation using differentiable ray-traced channels.
Takeaways & Limitations
Sionna RT integrates ray tracing, link-level simulations, and machine learning in one open-source framework for radio propagation research.
Abstract
from arXiv · showhide
Sionna is a GPU-accelerated open-source library for link-level simulations based on TensorFlow. Since release v0.14 it integrates a differentiable ray tracer (RT) for the simulation of radio wave propagation. This unique feature allows for the computation of gradients of the channel impulse response and other related quantities with respect to many system and environment parameters, such as material properties, antenna patterns, array geometries, as well as transmitter and receiver orientations and positions. In this paper, we outline the key components of Sionna RT and showcase example applications such as learning radio materials and optimizing transmitter orientations by gradient descent. While classic ray tracing is a crucial tool for 6G research topics like reconfigurable intelligent surfaces, integrated sensing and communications, as well as user localization, differentiable ray tracing is a key enabler for many novel and exciting research directions, for example, digital twins.
I. INTRODUCTION
Sionna RT addresses the need for spatially consistent radio-environment simulations by adding differentiable ray tracing to Sionna. It supports gradients of channel and coverage quantities with respect to many scene and system parameters.
- Motivation: Ray tracing is needed in 6G research to preserve spatial correspondence between physical scene locations and channel impulse responses.The cited applications include integrated sensing and communications, multimodal sensing, RIS, localization, and ML-based transceiver algorithms.
- Motivation: Stochastic channel models cannot provide the spatially consistent physical-location-to-channel correspondence required by these applications.
- Motivation: Digital twin networks are an additional motivation for increasing interest in ray tracing.
- Contribution: Sionna RT combines machine learning and ray tracing as a differentiable ray tracer for radio propagation modeling.
- Contribution: CIRs and coverage maps can be differentiated with respect to material properties, scattering parameters, array configurations, positions, orientations, and antenna patterns.
II. SIONNA RT
Sionna RT is a differentiable, GPU-accelerated ray-tracing extension built on Mitsuba 3 and TensorFlow for radio propagation modeling. It supports scene construction, rendering, configurable radio devices and materials, path tracing, coverage maps, and CIR generation for link-level simulations.
- Architecture: Sionna RT is built on Mitsuba 3 and TensorFlow and provides differentiable radio-propagation ray tracing through high-level Python APIs.It runs in Jupyter notebooks, uses available hardware such as NVIDIA GPUs, and is released under the Apache 2.0 license.
- Channel outputs: Computed CIRs can be used directly in time- or frequency-domain link-level simulations and can support end-to-end transceiver optimization.
- Architecture: Mitsuba 3 handles rendering, scene files, and ray intersections, while TensorFlow computes polarized interactions and combines paths into CIRs.TensorFlow automatic gradients make CIRs and functions derived from them differentiable.
- Scenes and rendering: Scenes can be created and exported with Blender or generated from OpenStreetMap, then rendered through configurable cameras or an integrated 3D viewer.
- Radio materials: Sionna RT supports arbitrary non-magnetic radio materials described by frequency-dependent permittivity, conductivity, and scattering parameters.Currently, non-magnetic materials use µr = 1.
- Antennas: Antenna arrays support custom positions and radiation patterns, with either explicit element tracing or synthetic plane-wave modeling after ray tracing.Explicit modeling is preferable for very large aperture arrays where the plane-wave assumption does not hold.
A. Code Examples
Sionna RT provides notebook-based workflows for configuring radio devices and antenna arrays, computing paths, coverage maps, and channel impulse responses, then visualizing the results.
- Computation and visualization: Sionna RT can render and explore scenes through configurable cameras, including viewpoints generated by changing camera positions and orientations.Scenes can also be rendered to files from the notebook preview.
- Configuration: Transmitters and receivers are configured with positions, orientations, shared antenna arrays, and customizable antenna patterns before propagation computation.Arrays may be single- or dual-polarized and can contain multiple identical antennas.
- Computation and visualization: Propagation paths and coverage maps can be computed for configured radio devices and displayed together in the integrated 3D preview.The preview can visualize paths and coverage maps within a Jupyter notebook.
- CIR generation: Computed paths can be transformed into CIRs, with Doppler evolution based on transmitter and receiver velocities and optional propagation phenomena deactivation.The resulting CIRs support link-level simulations in time or frequency domains.
B. Current Features & Planned Extensions
Sionna RT supports multiple propagation mechanisms while using alternative path-finding strategies, and future releases aim to add refraction and RIS support without losing differentiability.
- Current features: Sionna RT supports specular and diffuse reflections, scattering, and first-order diffraction.Reflected paths are computed with the image method using either exhaustive search or a heuristic ray-shooting method.
- Planned extensions: Future releases are planned to add refraction and reconfigurable intelligent surfaces while preserving differentiability throughout processing.
III. EXAMPLE APPLICATIONS
The paper presents two applications of differentiable ray tracing enabled by Sionna RT, with reproducible code provided for the reported results.
- Applications: Sionna RT enables two example applications of differentiable ray tracing.The code used to reproduce the results is available.
A. Learning Radio Materials
The application addresses the difficulty of assigning radio material properties to scene objects by optimizing trainable materials against generated channel responses. The discussion identifies practical scope limits for measured-data training and material sharing assumptions.
- Motivation: Assigning material properties to all objects is difficult even when scene geometries can be obtained from sources such as OpenStreetMap.Relevant properties include permittivity, conductivity, permeability, roughness, and scattering functions.
- Method: Trainable radio materials are optimized by gradient descent on normalized mean squared error between original and computed channel frequency responses.The materials are initialized with default values before optimization.
- Data generation: 400 receivers are placed within the scene, and CIRs are generated before being transformed to frequency-domain responses for training.
- Experimental setup: The experiment uses four scene materials—concrete, marble, metal, and wood—but omits metal results because no reflected path comes from a rooftop.
- Limitations: Training on channel frequency responses is unlikely to work well with measured data because ray tracing rarely predicts accurate phase information.
- Limitations: The approach assumes many objects share one material, whereas assigning materials per object or group would increase training complexity.
- Interpretation: Material learning can instead target the properties that best predict measured CIRs with the applied ray-tracing algorithm rather than physically correct material values.
B. Optimization of Transmitter Orientation
Differentiable ray tracing optimizes a transmitter’s orientation to maximize average received power in a selected scene region. The example computes orientation gradients from a coverage map and applies gradient ascent, while noting that such problems can become non-convex.
- The example optimizes a transmit array’s orientation to maximize average received signal power in a specific scene region.
- Gradients of the region’s average received power with respect to transmitter orientation are computed and optimized via gradient ascent.
- The experiment places a transmitter on a building and evaluates a small region behind the Arc de Triomphe using a coverage map.
- The authors note that these optimization problems may quickly become non-convex, requiring methods such as Bayesian optimization.
- Fig. 5 compares the full-scene coverage map before and after optimizing the transmitter orientation.
IV. CONCLUSIONS & FUTURE DIRECTIONS
The paper presents Sionna RT as a differentiable ray tracer that integrates ray tracing, link-level simulations, and machine learning in an open-source framework. It identifies digital twin networks as a major future ambition and research direction.
- Sionna RT is presented as the world’s first differentiable ray tracer for radio propagation modeling.
- The framework integrates ray tracing, link-level simulations, and machine learning into one open-source system.
- A major ambition is to make the creation of digital twin networks as simple as possible.