RadioFlow: Efficient Radio Map Construction Framework with Flow Matching
This work addresses efficiency bottlenecks in radio map construction for next-generation wireless systems, offering a scalable solution for real-time electromagnetic digital twins in 6G networks.
The paper tackles the problem of slow and large diffusion models for radio map generation by proposing RadioFlow, a flow-matching framework that achieves state-of-the-art performance with up to 8x fewer parameters and over 4x faster inference compared to baselines.
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}.