Scenario 18 simulates the trajectories of two receivers (associated with two pedestrian users) moving inside the LASSE laboratory environment, starting from opposite ends of the room and converging to a common final point. Throughout the route, each receiver represents a distinct propagation condition: one under line-of-sight (LOS) and the other under non-line-of-sight (NLOS); in the final segment, both become in direct visibility with the transmitter. This dataset was created specifically to validate a new Raymobtime feature focused on indoor simulations with antennas attached to pedestrians, replacing the traditional vehicle-based mobility model.


The LASSE indoor scenario models a realistic laboratory environment composed of two rooms connected by corridors openings and populated with a dense set of objects (e.g., desks, chairs, cabinets, shelves, and lab furniture). This rich clutter is intentionally included to increase fidelity with the real site, creating abundant reflecting and scattering surfaces and frequent occlusion opportunities typical of indoor deployments. A single transmitter (Tx) is placed inside the main lab area, while two mobile receivers (Rx #1 and Rx #2) represent pedestrian users carrying antenna devices.

Mobility is defined in SUMO using two independent pedestrian routes that start from opposite ends of the environment and move toward a final meeting region where both receivers eventually reach direct visibility (LOS) to the transmitter. Each route is modeled as a single-lane pedestrian path (number of lanes: 1) with a narrow effective width of 0.5 m, consistent with indoor walking corridors between furniture and structural elements. Both pedestrians move at a constant walking speed of 1.34 m/s. Route 1 has a total path length of 15.27 m, while Route 2 has a total path length of 16.5 m, yielding slightly different traversal times and spatial exposure to blockage and multipath

From a wireless-channel perspective, the environment behaves as an indoor canyon: walls, partitions, and large furniture create strong multipath through reflections and diffractions, while corners and obstructions induce transitions between NLOS and LOS conditions along the trajectories. In this scenario, one receiver is configured to predominantly experience LOS-like propagation over the route, whereas the other is designed to undergo more NLOS segments, with both converging to an LOS condition near the end of the paths. This combination of realistic geometry, dense object placement, and controlled pedestrian mobility makes the scenario representative for evaluating indoor beam management tasks such as beam tracking, blockage events, and robustness to rapid LOS/NLOS transitions.


The collected data was generated using the Raymobtime pipeline, which couples vehicular and ´pedestrian traffic simulation in SUMO with scene generation and ray-tracing in Remcom Wireless InSite (WI) via a Python orchestrator.

1. Ray tracing data: top 100 rays of highest received power, obtained from Wireless Insite (WI).

  • Received power, time of arrival, elevation angle of departure, azimuth angle of departure, elevation angle of arrival, azimuth angle of arrival, LOS condition.
  • Type data: .hdf5 of each episode
  • At database .db file is possible take rays paths and interactions information.

Raymobtime Scenario 14 – V2I Rosslyn 60 GHz