PILOT: One Physics-Integrated Generation Framework to Unify 2D and 3D Radio Map Construction
This work addresses the expensive and complex problem of radio map construction for network planning, digital twins, and UAV applications, offering a unified, efficient, and accurate solution.
PILOT introduces a physics-integrated autoregressive framework for unified 2D and 3D radio map construction, using a wavefront sequence and environment-aware instructions. It achieves the lowest NMSE on 2D benchmarks, reduces NMSE by 78% over diffusion baselines with 2500× faster inference, and excels in zero-shot cross-domain evaluation.
Unified 2D and 3D radio map construction supports network planning, wireless digital twins, and unmanned aerial vehicle (UAV) applications. In urban environments, blockage, reflection, and diffraction make accurate construction expensive for physics-based solvers. Autoregressive next-token prediction offers a single sequential formulation that can cover both 2D and 3D generation, but standard raster ordering ignores the spatial structure of radio propagation. When generation follows propagation, each token is predicted from propagation-relevant history rather than spatially arbitrary context, which provides more causally informative conditioning and lowers conditional uncertainty. We propose PILOT, a pretrained autoregressive framework that replaces raster scan with a wavefront sequence expanding outward from the transmitter. Each prediction step is guided by an environment-aware instruction that spatially aligns environment features with the queried radio map region. The same framework extends to 3D radio maps through height-slice stacking while a gradient loss enforces vertical continuity. On standard 2D benchmarks, PILOT achieves the lowest NMSE among all baselines. For volumetric generation, it reduces NMSE by 78% relative to the diffusion baseline at roughly $2500\times$ faster inference. It also outperforms methods that rely on 10% sparse measurements and achieves the best zero-shot results in the cross-domain evaluation.