@misc{9243953,
  abstract     = {{Wireless communication systems like Wi-Fi and 5G rely on radio waves whose behavior is difficult to interpret in indoor environments without spatial context. Raw channel measurements processed by parameter estimation algorithms like Space-Alternating Generalized Expectation-Maximization (SAGE) extract multipath components described by their angle of arrival, propagation delay, and attenuation. However, numerical tables fail to convey the intuitive spatial context of the measured data within the physical environment. While existing tools mostly rely on simulated data, to our knowledge, no system currently combines empirical multipath component data with physical room recordings for immersive Augmented Reality (AR) visualization.

This thesis builds an end-to-end pipeline to address this gap. A Light Detection and Ranging (LiDAR) scan of the room is reconstructed into a 3D mesh. Multipath components are cast against this mesh and geometrically classified as line-of-sight, single-bounce, multi-bounce, or out-of-bounds. These classified frames stream over User Datagram Protocol (UDP) to a Meta Quest 3 headset, where an Unreal Engine 5 application renders the rays in their correct world positions. A dedicated Hypertext Transfer Protocol (HTTP) service handles the initial setup by aligning the LiDAR scan with the headset's scene mesh.

The system successfully transforms empirical measurements into immersive 3D visualizations. Operating within the headset's performance budget, it achieves a reasonable spatial placement accuracy. Ultimately, this provides researchers with a visually plausible, intuitive tool to analyze wireless channels directly within the measured environment.}},
  author       = {{Paesschesoone, Wannes and Thys, Tom}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Wave Propagation in Augmented Reality}},
  year         = {{2026}},
}