Neural Gaussian Radio Fields for Channel Estimation

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 textitneural Gaussian radio fields (textcolorstanfordrednGRF), 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 textcolorstanfordrednGRF achieves 10.9 dB higher prediction SNR than state-of-the-art methods with 220× faster inference (1.1 ms vs. 242 ms), 18× lower measurement density, and 180× faster training. For large-scale outdoor environments where implicit methods fail, textcolorstanfordrednGRF achieves 28.32 dB SNR, demonstrating that structured representations supplemented by domain physics can fundamentally outperform generic deep learning architectures.