Wireless signals reveal building layouts without cameras or lidar
Scene Reconstruction Using Ubiquitous Wireless Signals via Diffusion Posterior Inference Over physics-Grounded Representations
Machine Learning
Summary
It can be hard to see the shape of buildings when cameras or lasers fail, like in darkness or bad weather. The authors show that wireless signals, like those from phones and base stations, carry hidden information about the buildings around them. By using a special method that respects how these signals bounce and travel, they turn signal data into guesses about where walls and buildings are. Combining many signals lets the method make better and clearer maps of the area, just from wireless signals alone.
What this means in practice
- •For emergency responders: Get building layouts behind walls or in dark environments using only wireless signals to improve rescue operations.
- •For telecommunications engineers: Enhance wireless network planning by reconstructing environments from signal data to better understand signal obstacles and reflections.
Authors
Vahid Yazdnian, Yasaman Ghasempour
Abstract
Knowing the layout of the physical world matters for many applications, yet the sensors we rely on for it (e.g., cameras and LiDAR) can fail in darkness, in adverse weather, or behind walls. Wireless signals do not face these limitations: every link between a base station and a user is shaped by the buildings it passes, reflects from, or is blocked by, leaving information about the surrounding scene embedded in the wireless channel measured at the endpoints. However, mapping channel measurements to the environment is a severely ill-posed problem: a measurement records only the structures that interact with the transmitted electromagnetic waves along its propagation paths, and even these observations can often be explained by multiple plausible scenes. We therefore formulate wireless scene reconstruction as posterior inference and develop a hierarchical framework with two complementary stages to approach this problem. A wireless foundation model, pretrained to respect propagation physics, transforms each channel measurement into a representation of the scene structure that shaped the observation. A diffusion model then jointly integrates representations from multiple measurements to model and sample the posterior over building occupancy, with consensus over samples producing the final reconstruction. We demonstrate that the framework can accurately recover building footprints from wireless channel measurements alone and show that its performance depends critically on both design components: physics-grounded representations expose scene-relevant information, while joint posterior inference coherently integrates complementary observations.