Bayesian-Driven Graph Reasoning for Active Radio Map Construction
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916911727509504 |
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| author | Lu, Wenlihan Gao, Shijian Wen, Miaowen Liang, Yuxuan Yang, Liuqing Chae, Chan-Byoung Poor, H. Vincent |
| author_facet | Lu, Wenlihan Gao, Shijian Wen, Miaowen Liang, Yuxuan Yang, Liuqing Chae, Chan-Byoung Poor, H. Vincent |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_09142 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bayesian-Driven Graph Reasoning for Active Radio Map Construction Lu, Wenlihan Gao, Shijian Wen, Miaowen Liang, Yuxuan Yang, Liuqing Chae, Chan-Byoung Poor, H. Vincent Signal Processing Artificial Intelligence 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. |
| title | Bayesian-Driven Graph Reasoning for Active Radio Map Construction |
| topic | Signal Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2508.09142 |