Bayesian-Driven Graph Reasoning for Active Radio Map Construction

2025-07-29

AI Summary

To address the challenge of constructing high-precision radio maps efficiently under stringent battery constraints for autonomous aerial vehicles in low-altitude economies, this paper proposes an uncertainty-aware radio map reconstruction framework. Methodologically, it integrates a Bayesian neural network to model spatial signal uncertainty in real time and designs an attention-enhanced reinforcement learning policy over a probabilistic road-network graph, enabling non-myopic waypoint planning that jointly optimizes safety constraints and information gain. The key contribution lies in the first synergistic coupling of Bayesian uncertainty estimation with attention-based reinforcement learning on graph-structured representations, facilitating global reasoning and energy-efficient data acquisition. Experiments demonstrate that the proposed method achieves up to a 34% improvement in radio map reconstruction accuracy over baseline approaches, while significantly enhancing coverage efficiency and information acquisition per unit energy consumption.

Technology Category

• Planning, Routing, and Scheduling: Planning under Uncertainty
• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Reconstructing radio maps with limited aerial agent battery capacity
• Addressing coverage and efficiency constraints in waypoint navigation
• Planning energy-efficient trajectories under spatial uncertainty constraints

Innovation

• Bayesian neural network for spatial uncertainty
• Attention-based reinforcement learning policy
• Graph reasoning for energy-efficient trajectory planning