Transfer Learning for CSI-based Positioning with Multi-environment Meta-learning
Utilizing deep learning (DL) techniques for radio-based positioning of user equipment (UE) through channel state information (CSI) fingerprints has demonstrated significant potential. DL models can extract complex characteristics from the CSI fingerprints of a particular environment and accurately p...
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Zusammenfassung: | Utilizing deep learning (DL) techniques for radio-based positioning of user
equipment (UE) through channel state information (CSI) fingerprints has
demonstrated significant potential. DL models can extract complex
characteristics from the CSI fingerprints of a particular environment and
accurately predict the position of a UE. Nonetheless, the effectiveness of the
DL model trained on CSI fingerprints is highly dependent on the particular
training environment, limiting the trained model's applicability across
different environments. This paper proposes a novel DL model structure
consisting of two parts, where the first part aims at identifying features that
are independent from any specific environment, while the second part combines
those features in an environment specific way with the goal of positioning. To
train such a two-part model, we propose the multi-environment meta-learning
(MEML) approach for the first part to facilitate training across various
environments, while the second part of the model is trained solely on data from
a specific environment. Our findings indicate that employing the MEML approach
for initializing the weights of the DL model for a new unseen environment
significantly boosts the accuracy of UE positioning in the new target
environment as well the reliability of its uncertainty estimation. This method
outperforms traditional transfer learning methods, whether direct transfer
learning (DTL) between environments or completely training from scratch with
data from a new environment. The proposed approach is verified with real
measurements for both line-of-sight (LOS) and non-LOS (NLOS) environments. |
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DOI: | 10.48550/arxiv.2405.11816 |