Sparse Gain Radio Map Reconstruction With Geometry Priors and Uncertainty-Guided Measurement Selection

2026-04-07

AI Summary

This work addresses the challenge of accurately reconstructing high-fidelity radio signal gain maps in complex urban environments from sparse measurements, where existing approaches inadequately exploit geometric priors and overlook the role of prediction uncertainty in guiding sensing. The authors propose GeoUQ-GFNet, a lightweight geometry-aware network that jointly integrates sparse measurements with structured scene geometry to simultaneously predict dense gain maps and spatial uncertainty maps, which in turn inform an active sampling strategy. This study presents the first unified, generalizable framework that explicitly combines geometric priors, uncertainty quantification, and adaptive measurement selection, and introduces UrbanRT-RM—a controllable and diverse urban ray-tracing benchmark. Experiments demonstrate that the proposed method significantly outperforms non-adaptive sampling strategies across various urban layouts and base station configurations, achieving substantially improved reconstruction quality under identical measurement budgets.

Abstract

Radio maps are important for environment-aware wireless communication, network planning, and radio resource optimization. However, dense radio map construction remains challenging when only a limited number of measurements are available, especially in complex urban environments with strong blockages, irregular geometry, and restricted sensing accessibility. Existing methods have explored interpolation, low-rank cartography, deep completion, and channel knowledge map (CKM) construction, but many of these methods insufficiently exploit explicit geometric priors or overlook the value of predictive uncertainty for subsequent sensing. In this paper, we study sparse gain radio map reconstruction from a geometry-aware and active sensing perspective. We first construct \textbf{UrbanRT-RM}, a controllable ray-tracing benchmark with diverse urban layouts, multiple base-station deployments, and multiple sparse sampling modes. We then propose \textbf{GeoUQ-GFNet}, a lightweight network that jointly predicts a dense gain radio map and a spatial uncertainty map from sparse measurements and structured scene priors. The predicted uncertainty is further used to guide active measurement selection under limited sensing budgets. Extensive experiments show that our proposed GeoUQ-GFNet method achieves strong and consistent reconstruction performance across different scenes and transmitter placements generated using UrbanRT-RM. Moreover, uncertainty-guided querying provides more effective reconstruction improvement than non-adaptive sampling under the same additional measurement budget. These results demonstrate the effectiveness of combining geometry-aware learning, uncertainty estimation, and benchmark-driven evaluation for sparse radio map reconstruction in complex urban environments.

Problem

• radio map reconstruction
• sparse measurements
• urban environments
• geometry priors
• uncertainty-guided sensing

Innovation

• geometry-aware learning
• uncertainty-guided active sensing
• sparse radio map reconstruction
• ray-tracing benchmark
• lightweight neural network