# NeWRF: A Deep Learning Framework for Wireless Radiation Field Reconstruction and Channel Prediction

arXiv: https://arxiv.org/abs/2403.03241 · HTML: https://mlsanity.com/paper/2403.03241/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2024.

## Built on by (most cited first, 28 of 28)

- [Data Can Speak for Itself: Quality-guided Utilization of Wireless Synthetic Data](https://mlsanity.com/paper/2506.23174/) (2025)
- [Generalizable Radio-Frequency Radiance Fields for Spatial Spectrum Synthesis](https://mlsanity.com/paper/2502.05708/) (2025)
- [Radio Propagation Modelling: To Differentiate or To Deep Learn, That Is The Question](https://mlsanity.com/paper/2509.19337/) (2025)
- [Wideband RF Radiance Field Modeling Using Frequency-embedded 3D Gaussian Splatting](https://mlsanity.com/paper/2505.20714/) (2025)
- [Can NeRFs See without Cameras?](https://mlsanity.com/paper/2505.22441/) (2025)
- [Taming Vision Priors for Data Efficient mmWave Channel Modeling](https://mlsanity.com/paper/2603.13383/) (2026)
- [Rasterizing Wireless Radiance Field via Deformable 2D Gaussian Splatting](https://mlsanity.com/paper/2506.12787/) (2025)
- [RF-PGS: Fully-structured Spatial Wireless Channel Representation with Planar Gaussian Splatting](https://mlsanity.com/paper/2508.16849/) (2025)
- [GSpaRC: Gaussian Splatting for Real-time Reconstruction of RF Channels](https://mlsanity.com/paper/2511.22793/) (2025)
- [An Efficient and Explainable KAN Framework for Wireless Radiation Field Prediction](https://mlsanity.com/paper/2601.11656/) (2026)
- [Point-Cloud-Assistant Localized Statistical Channel Prediction by Tangent Gaussian Splatting](https://mlsanity.com/paper/2606.18734/) (2026)
- [Point2Radio: A Foundation Model for Cross-Scene Radio Fields from Material-Aware Point Clouds](https://mlsanity.com/paper/2607.28994/) (2026)
- [AH-GS: Augmented 3D Gaussian Splatting for High-Frequency Detail Representation](https://mlsanity.com/paper/2503.22324/) (2025)
- [Terahertz Spatial Wireless Channel Modeling with Radio Radiance Field](https://mlsanity.com/paper/2505.06277/) (2025)
- [Neural Beam Field for Spatial Beam RSRP Prediction](https://mlsanity.com/paper/2508.06956/) (2025)
- [Radiance-Field Guided Pretraining: Scaling Localization Models with Unlabeled Wireless Signals](https://mlsanity.com/paper/2512.07309/) (2025)
- [Neural Electromagnetic Fields for High-Resolution Material Parameter Reconstruction](https://mlsanity.com/paper/2603.02582/) (2026)
- [A Geometric Algebra-informed NeRF Framework for Generalizable Wireless Channel Prediction](https://mlsanity.com/paper/2604.11983/) (2026)
- [Cross-Modal Generation: From Commodity WiFi to High-Fidelity mmWave and RFID Sensing](https://mlsanity.com/paper/2604.16558/) (2026)
- [Differentiable Ray Tracing with Gaussians for Unified Radio Propagation Simulation and View Synthesis](https://mlsanity.com/paper/2605.07781/) (2026)
- [GeRaF: Neural Geometry Reconstruction from Radio Frequency Signals](https://mlsanity.com/paper/2605.29097/) (2026)
- [RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling](https://mlsanity.com/paper/2605.29538/) (2026)
- [CORF-GS: Real-Time Wireless Radiance Field Reconstruction via Coupled Optical-RF Gaussian Splatting](https://mlsanity.com/paper/2607.25569/) (2026)
- [mmRadarTwin: A Measurement-Calibrated Signal-Level Digital Twin Platform for Indoor mmWave Radar](https://mlsanity.com/paper/2607.28108/) (2026)
- [Physics-Unrolled Neural Operator for Wireless Field Modeling](https://mlsanity.com/paper/2608.18495/) (2026)
- [3D Point Splatting for mmWave Radar Novel View Synthesis](https://mlsanity.com/paper/2609.11894/) (2026)
- [Geometry-Aided Channel Deduction with Partial Channel Estimates and Uncalibrated Digital Twin](https://mlsanity.com/paper/2609.37277/) (2026)
- [RFWeave: Learning Wireless Propagation Across Scenes and Transceiver Configurations](https://mlsanity.com/paper/2610.16656/) (2026)

## Abstract (arXiv)

We present NeWRF, a deep learning framework for predicting wireless channels. Wireless channel prediction is a long-standing problem in the wireless community and is a key technology for improving the coverage of wireless network deployments. Today, a wireless deployment is evaluated by a site survey which is a cumbersome process requiring an experienced engineer to perform extensive channel measurements. To reduce the cost of site surveys, we develop NeWRF, which is based on recent advances in Neural Radiance Fields (NeRF). NeWRF trains a neural network model with a sparse set of channel measurements, and predicts the wireless channel accurately at any location in the site. We introduce a series of techniques that integrate wireless propagation properties into the NeRF framework to account for the fundamental differences between the behavior of light and wireless signals. We conduct extensive evaluations of our framework and show that our approach can accurately predict channels at unvisited locations with significantly lower measurement density than prior state-of-the-art