RadioFlow: Efficient Radio Map Construction Framework with Flow Matching
By: Haozhe Jia , Wenshuo Chen , Xiucheng Wang and more
Potential Business Impact:
Makes wireless signals faster and uses less power.
Accurate and real-time radio map (RM) generation is crucial for next-generation wireless systems, yet diffusion-based approaches often suffer from large model sizes, slow iterative denoising, and high inference latency, which hinder practical deployment. To overcome these limitations, we propose \textbf{RadioFlow}, a novel flow-matching-based generative framework that achieves high-fidelity RM generation through single-step efficient sampling. Unlike conventional diffusion models, RadioFlow learns continuous transport trajectories between noise and data, enabling both training and inference to be significantly accelerated while preserving reconstruction accuracy. Comprehensive experiments demonstrate that RadioFlow achieves state-of-the-art performance with \textbf{up to 8$\times$ fewer parameters} and \textbf{over 4$\times$ faster inference} compared to the leading diffusion-based baseline (RadioDiff). This advancement provides a promising pathway toward scalable, energy-efficient, and real-time electromagnetic digital twins for future 6G networks. We release the code at \href{https://github.com/Hxxxz0/RadioFlow}{GitHub}.
Similar Papers
RadioDiff-Flux: Efficient Radio Map Construction via Generative Denoise Diffusion Model Trajectory Midpoint Reuse
Machine Learning (CS)
Makes wireless signals faster by reusing map parts.
Denoising Diffusion Probabilistic Model for Radio Map Estimation in Generative Wireless Networks
Networking and Internet Architecture
Creates fast wireless maps from little data.
Efficiency vs. Fidelity: A Comparative Analysis of Diffusion Probabilistic Models and Flow Matching on Low-Resource Hardware
Machine Learning (CS)
Makes AI create pictures much faster on phones.