{"ID":2889030,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.09142","arxiv_id":"2508.09142","title":"Bayesian-Driven Graph Reasoning for Active Radio Map Construction","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.","short_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...","url_abs":"https://arxiv.org/abs/2508.09142","url_pdf":"https://arxiv.org/pdf/2508.09142v2","authors":"[\"Wenlihan Lu\",\"Shijian Gao\",\"Miaowen Wen\",\"Yuxuan Liang\",\"Liuqing Yang\",\"Chan-Byoung Chae\",\"H. Vincent Poor\"]","published":"2025-07-29T03:32:01Z","proceeding":"eess.SP","tasks":"[\"eess.SP\",\"cs.AI\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}