Bayesian-Driven Graph Reasoning for Active Radio Map Construction

TL;DR

This paper introduces URAM, a Bayesian and graph-based framework for autonomous aerial agents to efficiently construct accurate radio maps by planning energy-efficient trajectories based on uncertainty estimates.

Contribution

It presents a novel uncertainty-aware radio map reconstruction method combining Bayesian neural networks and attention-based reinforcement learning for improved trajectory planning.

Findings

URAM improves reconstruction accuracy by up to 34%.

The framework enables energy-efficient, informative navigation.

Graph reasoning enhances non-myopic trajectory planning.

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.