用物理引导的神经模型实现复杂环境实时无线信道预测
Photon Splatting: A Physics-Guided Neural Surrogate for Real-Time Wireless Channel Prediction
- 引入携带方向波特征的虚拟光子，基于场景几何建模信号传播
- 推理延迟仅30毫秒，支持新发射位置和移动接收机无需重训练
- 适合6G网络规划与无线数字孪生系统，兼具实时性与可解释性
我们提出Photon Splatting，一种用于复杂环境中实时无线信道预测的物理引导神经代理模型。该框架引入附着于表面的虚拟源——光子，其携带由场景几何和发射端配置决定的方向性波特征。运行时，通过测地线栅格化器将这些光子投射到接收端的角域，生成信道冲激响应（CIR）。模型学习从发射-接收配置映射到完整信道响应的物理基础表征。训练完成后，可泛化至新的发射位置、天线波束模式及移动接收机，无需重新训练。实验涵盖典型3D场景和包含1000个接收机的复杂室内咖啡厅。结果表明，推理延迟低至30毫秒，且在多种配置下均能实现高精度的CIR预测。该方法支持实时适应与可解释性，是无线数字孪生平台与未来6G网络规划的有力候选方案。
原文摘要 · Abstract (English)
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.
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