# RayLoc: Wireless Indoor Localization via Fully Differentiable Ray-tracing

arXiv: https://arxiv.org/abs/2501.17881 · HTML: https://mlsanity.com/paper/2501.17881/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2025.

## Built on by (most cited first, 3 of 3)

- [RadioDiff-Loc: Diffusion Model Enhanced Scattering Congnition for NLoS Localization with Sparse Radio Map Estimation](https://mlsanity.com/paper/2509.01875/) (2025)
- [GLocFM: A Geometry-Aware Foundation Model for 3D Indoor Wireless Localization](https://mlsanity.com/paper/2608.09285/) (2026)
- [Bridging the Data Gap: Digital Twin as a New Paradigm for AI-based Radio Sensing](https://mlsanity.com/paper/2609.26214/) (2026)

## Abstract (arXiv)

Wireless indoor localization has been a pivotal area of research over the last two decades, becoming a cornerstone for numerous sensing applications. However, conventional wireless localization methods rely on channel state information to perform blind modelling and estimation of a limited set of localization parameters. This oversimplification neglects many sensing scene details, resulting in suboptimal localization accuracy. To address this limitation, this paper presents a novel approach to wireless indoor localization by reformulating it as an inverse problem of wireless ray-tracing, inferring scene parameters that generates the measured CSI. At the core of our solution is a fully differentiable ray-tracing simulator that enables backpropagation to comprehensive parameters of the sensing scene, allowing for precise localization. To establish a robust localization context, RayLoc constructs a high-fidelity sensing scene by refining coarse-grained background model. Furthermore, RayLoc overcomes the challenges of sparse gradient and local minima by convolving the signal generation process with a Gaussian kernel. Extensive experiments showcase that RayLoc outperforms traditional localization baselines and is able to generalize to different sensing environments.