LIDAR-Assisted Channel Modelling for LiFi in Realistic Indoor Scenarios

We present a new, accurate, low complexity channel modelling methodology for LiFi in realistic indoor scenarios. A LIDAR scanner is used to capture the 3D environment in which the LiFi system is to be deployed. Next, the generated 3D point cloud data is pre-processed to determine the reflectance par...

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Veröffentlicht in:IEEE access 2022, Vol.10, p.59383-59399
Hauptverfasser: Mana, Sreelal M., Gabra, Kerolos G. K., Kouhini, Sepideh M., Hinrichs, Malte, Schulz, Dominic, Hellwig, Peter, Paraskevopoulos, Anagnostis, Freund, Ronald, Jungnickel, Volker
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Sprache:eng
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Zusammenfassung:We present a new, accurate, low complexity channel modelling methodology for LiFi in realistic indoor scenarios. A LIDAR scanner is used to capture the 3D environment in which the LiFi system is to be deployed. Next, the generated 3D point cloud data is pre-processed to determine the reflectance parameters of the walls and objects in the room. This is easier and more realistic than the manual definition of the environment, which is the current state of the art. As an additional innovation, the complexity of the channel modelling is reduced by: 1) modelling the line-of-sight and initial reflections precisely in the frequency-domain; 2) using a well-established analytical model based on the integrating sphere for all higher-order diffuse reflections. All steps together yield a substantially simplified channel modelling approach and model the links between multiple optical frontends and multiple mobile devices realistically. As a validation of our new approach, we compare measurements and simulations in two indoor scenarios: an empty room and a conference room with furniture. Simulations and measurements show excellent agreement with a mean square error below 3 percent. Moreover, we evaluate the performance of a distributed multiuser multiple-input multiple-output (MIMO) link and found excellent agreement between the model and measurements. Finally, we discuss the fundamental trade-off between complexity and model error, which depends on the scenario.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3176353