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  4. Transfer Learning to adapt 5G AI-based Fingerprint Localization across Environments
 
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2022
Conference Paper
Title

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

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.
Author(s)
Stahlke, Maximilian
Fraunhofer-Institut für Integrierte Schaltungen IIS  
Feigl, Tobias  
Fraunhofer-Institut für Integrierte Schaltungen IIS  
Castañeda García, Mario H.
Stirling-Gallacher, Richard A.
Seitz, Jochen  
Fraunhofer-Institut für Integrierte Schaltungen IIS  
Mutschler, Christopher  
Fraunhofer-Institut für Integrierte Schaltungen IIS  
Mainwork
IEEE 95th Vehicular Technology Conference, VTC2022-Spring. Proceedings  
Conference
Vehicular Technology Conference (VTC Spring) 2022  
DOI
10.1109/VTC2022-Spring54318.2022.9860906
Language
English
Fraunhofer-Institut für Integrierte Schaltungen IIS  
Keyword(s)
  • CNN

  • CSI

  • QuaDRiGa

  • Transfer Learning

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