Stahlke, MaximilianMaximilianStahlkeFeigl, TobiasTobiasFeiglCastañeda García, Mario H.Mario H.Castañeda GarcíaStirling-Gallacher, Richard A.Richard A.Stirling-GallacherSeitz, JochenJochenSeitzMutschler, ChristopherChristopherMutschler2023-07-132023-07-132022https://publica.fraunhofer.de/handle/publica/44557110.1109/VTC2022-Spring54318.2022.98609062-s2.0-85137742899Fingerprint-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.enCNNCSIQuaDRiGaTransfer Learningconference paper