Accurate channel state information (CSI) is a critical bottleneck in modern wireless networks, with pilot overhead consuming 11\% to 21\% of transmission bandwidth and feedback delays causing severe throughput degradation under mobility. Addressing this requires rethinking how neural fields represent coherent wave phenomena. This work introduces \textit{neural Gaussian radio fields (\textcolor{stanfordred}{nGRF})}, a physics-informed framework that fundamentally reframes neural field design by replacing view-dependent rasterization with direct complex-valued aggregation in 3D space. This approach natively models wave superposition rather than visual occlusion. The architectural shift transforms the learning objective from function-fitting to source-recovery, a well-posed inverse problem grounded in electromagnetic theory. While demonstrated for wireless channel estimation, the core principle of explicit primitive-based fields with physics-constrained aggregation extends naturally to any coherent wave-based domain, including acoustic propagation, seismic imaging, and ultrasound reconstruction. Evaluations show that the inductive bias of \textcolor{stanfordred}{nGRF} achieves 10.9 dB higher prediction SNR than state-of-the-art methods with 220$\times$ faster inference (1.1 ms vs. 242 ms), 18$\times$ lower measurement density, and 180$\times$ faster training. For large-scale outdoor environments where implicit methods fail, \textcolor{stanfordred}{nGRF} achieves 28.32 dB SNR, demonstrating that structured representations supplemented by domain physics can fundamentally outperform generic deep learning architectures.

翻译：精确的信道状态信息（CSI）是现代无线网络的关键瓶颈，导频开销消耗了11%至21%的传输带宽，而反馈延迟在移动环境下会导致严重的吞吐量下降。解决这一问题需要重新思考神经场如何表示相干波现象。本文提出了\textit{神经高斯无线场（\textcolor{stanfordred}{nGRF}）}，这是一个基于物理信息的框架，它通过用三维空间中的直接复数值聚合取代视图依赖的光栅化，从根本上重构了神经场的设计。该方法本质上是建模波的叠加，而非视觉遮挡。这一架构转变将学习目标从函数拟合转变为源恢复，这是一个基于电磁理论的适定逆问题。虽然本文针对无线信道估计进行了演示，但基于显式基元且具有物理约束聚合的核心原理，可以自然地扩展到任何基于相干波的领域，包括声学传播、地震成像和超声重建。评估结果表明，\textcolor{stanfordred}{nGRF}的归纳偏置比最先进方法实现了10.9 dB更高的预测信噪比，推理速度快220倍（1.1 ms vs. 242 ms），测量密度低18倍，训练速度快180倍。在隐式方法失效的大规模室外环境中，\textcolor{stanfordred}{nGRF}实现了28.32 dB的信噪比，这表明由领域物理知识补充的结构化表示能够从根本上超越通用的深度学习架构。