With the emergence of the low-altitude economy, radio maps have become essential for ensuring reliable wireless connectivity to aerial platforms. Autonomous aerial agents are commonly deployed for data collection using waypoint-based navigation; however, their limited battery capacity significantly constrains coverage and efficiency. To address this, we propose an uncertainty-aware radio map (URAM) reconstruction framework that explicitly leverages graph-based reasoning tailored for waypoint navigation. Our approach integrates two key deep learning components: (1) a Bayesian neural network that estimates spatial uncertainty in real time, and (2) an attention-based reinforcement learning policy that performs global reasoning over a probabilistic roadmap, using uncertainty estimates to plan informative and energy-efficient trajectories. This graph-based reasoning enables intelligent, non-myopic trajectory planning, guiding agents toward the most informative regions while satisfying safety constraints. Experimental results show that URAM improves reconstruction accuracy by up to 34% over existing baselines.
@article{arxiv.2508.09142,
title = {Bayesian-Driven Graph Reasoning for Active Radio Map Construction},
author = {Wenlihan Lu and Shijian Gao and Miaowen Wen and Yuxuan Liang and Liuqing Yang and Chan-Byoung Chae and H. Vincent Poor},
journal= {arXiv preprint arXiv:2508.09142},
year = {2025}
}