iRadioDiff: Physics-Informed Diffusion Model for Indoor Radio Map Construction and Localization

TL;DR

iRadioDiff is a physics-informed, diffusion-based framework that accurately constructs indoor radio maps and improves localization by incorporating environment-specific physical properties and multipath effects without requiring extensive sampling.

Contribution

The paper introduces iRadioDiff, a novel sampling-free diffusion model that leverages physics-informed prompts and multipath priors for high-fidelity indoor radio map construction.

Findings

Achieves state-of-the-art accuracy in indoor radio map construction.

Provides effective generalization across different indoor layouts and materials.

Enhances localization performance using physically consistent radio maps.

Abstract

Radio maps (RMs) serve as environment-aware electromagnetic (EM) representations that connect scenario geometry and material properties to the spatial distribution of signal strength, enabling localization without costly in-situ measurements. However, constructing high-fidelity indoor RMs remains challenging due to the prohibitive latency of EM solvers and the limitations of learning-based methods, which often rely on sparse measurements or assumptions of homogeneous material, which are misaligned with the heterogeneous and multipath-rich nature of indoor environments. To overcome these challenges, we propose iRadioDiff, a sampling-free diffusion-based framework for indoor RM construction. iRadioDiff is conditioned on access point (AP) positions, and physics-informed prompt encoded by material reflection and transmission coefficients. It further incorporates multipath-critical priors, including diffraction points, strong transmission boundaries, and line-of-sight (LoS) contours, to guide the generative process via conditional channels and boundary-weighted objectives. This design enables accurate modeling of nonstationary field discontinuities and efficient construction of physically consistent RMs. Experiments demonstrate that iRadioDiff achieves state-of-the-art performance in indoor RM construction and received signal strength based indoor localization, which offers effective generalization across layouts and material configurations. Code is available at https://github.com/UNIC-Lab/iRadioDiff.