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 (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 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, nGRF achieves 28.32 dB SNR, demonstrating that structured representations supplemented by domain physics can fundamentally outperform generic deep learning architectures.

翻译：准确的信道状态信息（CSI）是现代无线网络中的关键瓶颈，导频开销消耗了传输带宽的11%至21%，反馈延迟在移动场景下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\textit{神经高斯无线电场（nGRF）}，这是一种物理信息驱动的框架，通过将视图依赖的光栅化替换为三维空间中的直接复数值聚合，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——一个基于电磁理论的适定逆问题。虽然本文以无线信道估计为例进行验证，但基于显式基元且具有物理约束聚合的核心原理可自然扩展到任何相干波领域，包括声学传播、地震成像和超声重建。评估结果表明，nGRF的归纳偏置相比最先进方法实现了10.9 dB更高的预测信噪比，推理速度提升220倍（1.1 ms对比242 ms），测量密度降低18倍，训练速度提升180倍。在隐式方法失效的大规模室外环境中，nGRF达到了28.32 dB的信噪比，证明结合领域物理的结构化表示能够从根本上超越通用的深度学习架构。