SIGN-RF: Self-Adaptive Neural Fields for Scalable Urban Radio Reconstruction

Accurate modeling of urban-scale wireless channels is critical for the next-generation 5G/6G communication systems. Conventional approaches, both deterministic and statistical are inherently limited by a trade-off between accuracy and computational efficiency. While recent advances in neural radiance fields (NeRF2) have shown promise for indoor channel prediction, their extension to urban-scale environments remains infeasible due to the need for large-scale labeled measurements and the substantial computational overhead required for high-resolution radiance field representations. To address these challenges, we propose SIGN-RF, a self-adaptive neural field framework for urban-scale radio reconstruction. SIGN-RF introduces two key innovations: (1) adaptive hierarchical sampling that autonomously identifies electromagnetically significant regions without environmental priors, and (2) self-supervised position labeling that efficiently associates unpositioned RF measurements with spatial coordinates, thereby maximizing the utility of heterogeneous measurement streams. Comprehensive real-world experiments show that SIGN-RF achieves state-of-the-art accuracy in large-scale outdoor radio reconstruction, converging 67% faster than existing NeRF2, while requiring as few as 20% positioned measurements. These results underscore the scalability and data efficiency of SIGN-RF for urban wireless channel modeling.