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AI Pseudo-Cell: A Method for Generating MultiCell Measurements for Precise Positioning using Language Translation Models
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- Authors
- Jin Ah Kang, Youngsu Cho, Juil Jeon, Jung Ho Lee, Sun Sim Chun
- Issue Date
- 2025-09
- Citation
- International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+) 2025, pp.1687-1694
- Language
- English
- Type
- Conference Paper
- Abstract
- This paper proposes a novel and practical method, termed artificial intelligence (AI) Pseudo-Cell, designed to enhance the accuracy of cell-based positioning in mobile networks. The approach augments conventional cell measurements (CMs), which are commonly used in existing cell-based positioning methods. Specifically, AI Pseudo-Cell employs neural machine translation (NMT) models-typically used in natural language processing-to generate neighboring CMs from other mobile network operators (MNOs), using the CMs of the subscribed MNO connected to a user equipment (UE) as input. The generated multi-MNO CMs can be effectively utilized to enhance the positioning precision compared to using a single-MNO's CMs. To implement AI Pseudo-Cell, we collected multi-MNO CMs from diverse geographic locations using a custom-built device equipped with three long term evolution (LTE) modems, each subscribed to a different MNO. Based on the collected data, multiple NMT models were trained to translate CMs between MNOs at the same location. Experimental results demonstrate that the AI Pseudo-Cell model can generate CMs from other MNOs with an average accuracy over 71% across training, evaluation, and indoor datasets, while achieving an average cell ID match rate of 86%. These findings suggest that the AI Pseudo-Cell can enable precise location inference even in challenging environments such as global navigation satellite system (GNSS) shadow zones.
- KSP Keywords
- Global Navigation satellite system(GNSS), Machine Translation(MT), Mobile Network Operator(MNO), Natural Language processing, Neural machine translation, Positioning Precision, Practical method, Precise location, Translation Model, User equipment(UE), artificial intelligence