Bayesian-Driven Graph Reasoning for Active Radio Map Construction

2025/07/29 by Wencheng Lu, Shijian Gao, Lu, Wenlihan +11 · 2 citations
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Electrical engineering #Robotic Path Planning Algorithms #Robotics and Sensor-Based Localization #Signal Processing (eess.SP) #UAV Applications and Optimization #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2508.09142

openalex publication_date 2025/07/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G
• Dataset of Pathloss and ToA Radio Maps With Localization Application
• Sionna: An Open-Source Library for Next-Generation Physical Layer Research
• Spectrum Surveying: Active Radio Map Estimation with Autonomous UAVs
• Lightweight UAV-based Measurement System for Air-to-Ground Channels at\n 28 GHz
• Deep Completion Autoencoders for Radio Map Estimation
• An informative path planning framework for UAV-based terrain monitoring
• Proximal Policy Optimization Algorithms
• What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
• Stochastic Variational Deep Kernel Learning
• The CMA Evolution Strategy: A Tutorial
• Probabilistic roadmaps for path planning in high-dimensional configuration spaces

Cited by

• Relaying Signal When Monitoring Traffic: Double Use of Aerial Vehicles Towards Intelligent Low-Altitude Networking
• Flow Matching-Based Active Learning for Radio Map Construction with Low-Altitude UAVs

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• Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on Graphs