Bayesian-Driven Graph Reasoning for Active Radio Map Construction

• URL: http://arxiv.org/abs/2508.09142v2
• Date: Fri, 22 Aug 2025 07:05:54 GMT
• Title: Bayesian-Driven Graph Reasoning for Active Radio Map Construction
• Authors: Wenlihan Lu, Shijian Gao, Miaowen Wen, Yuxuan Liang, Liuqing Yang, Chan-Byoung Chae, H. Vincent Poor,
• Abstract summary: We propose an uncertainty-aware radio map reconstruction framework that explicitly leverages graph-based reasoning tailored for waypoint navigation.<n>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.<n> Experimental results show that URAM improves reconstruction accuracy by up to 34% over existing baselines.
• Score: 96.08082552413117
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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