A Step Closer Towards 5G mmWave-Based Multipath Positioning in Dense Urban Environments

Qamar Bader, Sharief Saleh, Mohamed Elhabiby, Aboelmagd Noureldin

Peer Reviewed

Abstract: 5G mmWave technology can turn multipath into a friend, as multipath components become highly resolvable in the time and angle domains. Multipath signals have not only been used in the literature to position the user equipment (UE), but also to create a map of the surrounding environment. Yet, many multipath-based methods in the literature share a common assumption, which entails that multipath signals are caused by single-bounce reflections only, which is not usually the case. There are very few methods in the literature that accurately filters out higher-order reflections, which renders the exploitation of multipath signals challenging. This paper proposes an ensemble learning-based model for classifying signal paths based on their order of reflection using 5G channel parameters. The model is trained on a large dataset of 3.6 million observations obtained from a quasi-real ray-tracing based 5G simulator that utilizes 3D maps of real-world downtown environments. The trained model had a testing accuracy of 99.5%. A single-bounce reflection-based positioning method was used to validate the positioning error. The trained model enabled the positioning solution to maintain sub-30cm level accuracy 97% of the time.
Published in: Proceedings of the 36th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2023)
September 11 - 15, 2023
Hyatt Regency Denver
Denver, Colorado
Pages: 2419 - 2430
Cite this article: Bader, Qamar, Saleh, Sharief, Elhabiby, Mohamed, Noureldin, Aboelmagd, "A Step Closer Towards 5G mmWave-Based Multipath Positioning in Dense Urban Environments," Proceedings of the 36th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2023), Denver, Colorado, September 2023, pp. 2419-2430. https://doi.org/10.33012/2023.19246
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