{"ID":2886036,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.11668","arxiv_id":"2508.11668","title":"Neural Gaussian Radio Fields for Channel Estimation","abstract":"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.","short_abstract":"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 phen...","url_abs":"https://arxiv.org/abs/2508.11668","url_pdf":"https://arxiv.org/pdf/2508.11668v3","authors":"[\"Muhammad Umer\",\"Muhammad Ahmed Mohsin\",\"Ahsan Bilal\",\"John M. Cioffi\"]","published":"2025-08-06T23:49:06Z","proceeding":"eess.SP","tasks":"[\"eess.SP\",\"cs.NI\"]","methods":"[]","has_code":false}