City-Scale Radiance-Field Reconstruction via Mixture of Radio Experts

摘要

Accurately modeling the radiance field of 5G signals in urban environments is essential for reliable network operation and scalable planning of next-generation wireless systems. Recent advances in neural radiance fields (NeRF2) have shown great success in capturing the spatially continuous behavior of radio propagation. However, existing NeRF2 frameworks remain limited to small-scale scenes, suffering from severe accuracy degradation and prohibitive computational costs when extended to city-scale deployments. In this work, we present Mixture of Radio Experts (MoRE), a mixture-of-experts architecture designed for city-scale 5G radiance field reconstruction. MoRE integrates two complementary components: (i) a Mixture of Cell Experts, where each expert captures localized attenuation effects within distinct geographic regions; and (ii) a Mixture of Radiance Experts, which leverages shared and dynamically routed experts to model diverse propagation phenomena. To evaluate MoRE, we conducted a large-scale measurement campaign comprising 7,073,565 timestamped and geotagged RF samples from 13,602 base stations across 1,900 km2 in a tier-one city. Experimental results show that MoRE delivers up to a 16 dB improvement over standard NeRF2 by scaling from 0.69 million to 300 million parameters, while activating only 0.28% of the parameters (about 0.85 million) per inference.