Transfer Learning to adapt 5G AI-based Fingerprint Localization across Environments

Stahlke M, Feigl T, Castañeda García MH, Richard A. SG, Seitz J, Mutschler C (2022)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2022

Pages Range: 1-6

Conference Proceedings Title: Proc. Intl. Conf. IEEE Vehicular Technology Conference (VTC-Spring)

Event location: Helsinki FI

DOI: 10.1109/VTC2022-Spring54318.2022.9860906

Abstract

Fingerprint-based indoor positioning has attracted a lot of interest due to its potential to meet a positional accuracy that enables many location-based 5G indoor services. However, the accuracy of fingerprinting decreases with changes in the environment which prevents positioning in new scenarios. On the other hand, naively acquiring up-to-date training data from the changed environment to retrain the model is often time-consuming. It is unclear whether after a change in the environment, a fingerprint model can be (data-)efficiently updated.This paper examines the generalizability (with respect to accuracy, robustness, and effort in recording data) of state-of-the-art fingerprint models based on a convolutional neural network (CNN) in realistic setups with changes in the environment. We propose a transfer learning (TL) method that exploits realistic synthetic Channel State Information (CSI) obtained with the Quasi Deterministic Radio channel Generator (QuaDRiGa), used to pre-train the CNN-based fingerprint model so that it can be adapted to any real (NLoS) propagation scenario with a low number of real training samples. Our experiments show that the positioning accuracy using fine-tuning improves by 37% in changed and by 19% in new environments.

Authors with CRIS profile

Maximilian Stahlke Machine Learning and Data Analytics Lab Tobias Feigl Lehrstuhl für Informatik 2 (Programmiersysteme)

Involved external institutions

Fraunhofer-Institut für Integrierte Schaltungen (IIS) DE Germany (DE)

How to cite

APA:

Stahlke, M., Feigl, T., Castañeda García, M.H., Richard A., S.-G., Seitz, J., & Mutschler, C. (2022). Transfer Learning to adapt 5G AI-based Fingerprint Localization across Environments. In Proc. Intl. Conf. IEEE Vehicular Technology Conference (VTC-Spring) (pp. 1-6). Helsinki, FI.

MLA:

Stahlke, Maximilian, et al. "Transfer Learning to adapt 5G AI-based Fingerprint Localization across Environments." Proceedings of the IEEE 95th Vehicular Technology Conference (VTC-Spring), Helsinki 2022. 1-6.

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