Photon Splatting: A Physics-Guided Neural Surrogate for Real-Time Wireless Channel Prediction
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911042174451712 |
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| author | Cao, Ge Gradoni, Gabriele Peng, Zhen |
| author_facet | Cao, Ge Gradoni, Gabriele Peng, Zhen |
| contents | We present Photon Splatting, a physics-guided neural surrogate model for real-time wireless channel prediction in complex environments. The proposed framework introduces surface-attached virtual sources, referred to as photons, which carry directional wave signatures informed by the scene geometry and transmitter configuration. At runtime, channel impulse responses (CIRs) are predicted by splatting these photons onto the angular domain of the receiver using a geodesic rasterizer. The model is trained to learn a physically grounded representation that maps transmitter-receiver configurations to full channel responses. Once trained, it generalizes to new transmitter positions, antenna beam patterns, and mobile receivers without requiring model retraining. We demonstrate the effectiveness of the framework through a series of experiments, from canonical 3D scenes to a complex indoor cafe with 1,000 receivers. Results show 30 millisecond-level inference latency and accurate CIR predictions across a wide range of configurations. The approach supports real-time adaptability and interpretability, making it a promising candidate for wireless digital twin platforms and future 6G network planning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04595 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Photon Splatting: A Physics-Guided Neural Surrogate for Real-Time Wireless Channel Prediction Cao, Ge Gradoni, Gabriele Peng, Zhen Machine Learning We present Photon Splatting, a physics-guided neural surrogate model for real-time wireless channel prediction in complex environments. The proposed framework introduces surface-attached virtual sources, referred to as photons, which carry directional wave signatures informed by the scene geometry and transmitter configuration. At runtime, channel impulse responses (CIRs) are predicted by splatting these photons onto the angular domain of the receiver using a geodesic rasterizer. The model is trained to learn a physically grounded representation that maps transmitter-receiver configurations to full channel responses. Once trained, it generalizes to new transmitter positions, antenna beam patterns, and mobile receivers without requiring model retraining. We demonstrate the effectiveness of the framework through a series of experiments, from canonical 3D scenes to a complex indoor cafe with 1,000 receivers. Results show 30 millisecond-level inference latency and accurate CIR predictions across a wide range of configurations. The approach supports real-time adaptability and interpretability, making it a promising candidate for wireless digital twin platforms and future 6G network planning. |
| title | Photon Splatting: A Physics-Guided Neural Surrogate for Real-Time Wireless Channel Prediction |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.04595 |