# Learning Radio Environments by Differentiable Ray Tracing

arXiv: https://arxiv.org/abs/2311.18558 · HTML: https://mlsanity.com/paper/2311.18558/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 55 references that are in the map.
- First posted on arXiv 30 Nov 2023.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [Neuralangelo: High-Fidelity Neural Surface Reconstruction](https://mlsanity.com/paper/2306.03092/) (2023)
- [The Loss Surfaces of Multilayer Networks](https://mlsanity.com/paper/1412.0233/) (2014)
- [RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks](https://mlsanity.com/paper/1911.09002/) (2019)
- [Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling](https://mlsanity.com/paper/2303.11103/) (2023)
- [Toward a 6G AI-Native Air Interface](https://mlsanity.com/paper/2012.08285/) (2020)
- [DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset](https://mlsanity.com/paper/2211.09769/) (2022)
- [Channel Charting: Locating Users within the Radio Environment using Channel State Information](https://mlsanity.com/paper/1807.05247/) (2018)
- [Real-Time Digital Twins: Vision and Research Directions for 6G and Beyond](https://mlsanity.com/paper/2301.11283/) (2023)
- [6G Digital Twin Networks: From Theory to Practice](https://mlsanity.com/paper/2212.02032/) (2022)
- [Cellular Network Radio Propagation Modeling with Deep Convolutional Neural Networks](https://mlsanity.com/paper/2110.01848/) (2021)
- [6G White Paper on Localization and Sensing](https://mlsanity.com/paper/2006.01779/) (2020)
- [A Distributed Massive MIMO Channel Sounder for "Big CSI Data"-driven Machine Learning](https://mlsanity.com/paper/2206.15302/) (2022)
- [Machine Learning-based Urban Canyon Path Loss Prediction using 28 GHz Manhattan Measurements](https://mlsanity.com/paper/2202.05107/) (2022)
- [Tuning of Ray-Based Channel Model for 5G Indoor Industrial Scenarios](https://mlsanity.com/paper/2309.06101/) (2023)

## Built on by (most cited first, 23 of 23)

- [Calibrating Wireless Ray Tracing for Digital Twinning using Local Phase Error Estimates](https://mlsanity.com/paper/2312.12625/) (2023)
- [Differentiable and Learnable Wireless Simulation with Geometric Transformers](https://mlsanity.com/paper/2406.14995/) (2024)
- [How to Bridge the Sim-to-Real Gap in Digital Twin-Aided Telecommunication Networks](https://mlsanity.com/paper/2507.07067/) (2025)
- [Model-based learning for multi-antenna multi-frequency location-to-channel mapping](https://mlsanity.com/paper/2407.07719/) (2024)
- [Differentiable High-Performance Ray Tracing-Based Simulation of Radio Propagation with Point Clouds](https://mlsanity.com/paper/2507.04021/) (2025)
- [Taming Vision Priors for Data Efficient mmWave Channel Modeling](https://mlsanity.com/paper/2603.13383/) (2026)
- [Towards Generative Ray Path Sampling for Faster Point-to-Point Ray Tracing](https://mlsanity.com/paper/2410.23773/) (2024)
- [SANDWICH: Towards an Offline, Differentiable, Fully-Trainable Wireless Neural Ray-Tracing Surrogate](https://mlsanity.com/paper/2411.08767/) (2024)
- [AIRMap: AI-Generated Radio Maps for Wireless Digital Twins](https://mlsanity.com/paper/2511.05522/) (2025)
- [Vision-Language-Model-Guided Differentiable Ray Tracing for Fast and Accurate Multi-Material RF Parameter Estimation](https://mlsanity.com/paper/2601.18242/) (2026)
- [Heterogeneity-agnostic AI/ML-assisted beam selection for multi-panel arrays](https://mlsanity.com/paper/2602.18678/) (2026)
- [Simulating, Fast and Slow: Learning Policies for Black-Box Optimization](https://mlsanity.com/paper/2406.04261/) (2024)
- [Comparing Differentiable and Dynamic Ray Tracing: Introducing the Multipath Lifetime Map](https://mlsanity.com/paper/2410.14535/) (2024)
- [End-to-End Human Pose Reconstruction from Wearable Sensors for 6G Extended Reality Systems](https://mlsanity.com/paper/2503.04860/) (2025)
- [Model-based Implicit Neural Representation for sub-wavelength Radio Localization](https://mlsanity.com/paper/2506.06387/) (2025)
- [Semantically Annotated Multimodal Dataset for RF Interpretation and Prediction](https://mlsanity.com/paper/2604.01433/) (2026)
- [Differentiable Ray Tracing with Gaussians for Unified Radio Propagation Simulation and View Synthesis](https://mlsanity.com/paper/2605.07781/) (2026)
- [DIFFRACT: Neuralized Utility Maximization for Wireless Networks by Differentiable Programming](https://mlsanity.com/paper/2606.07114/) (2026)
- [mmRadarTwin: A Measurement-Calibrated Signal-Level Digital Twin Platform for Indoor mmWave Radar](https://mlsanity.com/paper/2607.28108/) (2026)
- [From Passive Mirrors to Active Agents: Holonic Digital Twins for Physical AI over Networks](https://mlsanity.com/paper/2608.06227/) (2026)
- [Physics-Unrolled Neural Operator for Wireless Field Modeling](https://mlsanity.com/paper/2608.18495/) (2026)
- [Should I Use This Synthetic Dataset for Training? How to Test with Minimal Real Data](https://mlsanity.com/paper/2608.27996/) (2026)
- [Geometry-Aided Channel Deduction with Partial Channel Estimates and Uncalibrated Digital Twin](https://mlsanity.com/paper/2609.37277/) (2026)

## Abstract (arXiv)

Ray tracing (RT) is instrumental in 6G research in order to generate spatially-consistent and environment-specific channel impulse responses (CIRs). While acquiring accurate scene geometries is now relatively straightforward, determining material characteristics requires precise calibration using channel measurements. We therefore introduce a novel gradient-based calibration method, complemented by differentiable parametrizations of material properties, scattering and antenna patterns. Our method seamlessly integrates with differentiable ray tracers that enable the computation of derivatives of CIRs with respect to these parameters. Essentially, we approach field computation as a large computational graph wherein parameters are trainable akin to weights of a neural network (NN). We have validated our method using both synthetic data and real-world indoor channel measurements, employing a distributed multiple-input multiple-output (MIMO) channel sounder.