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Recent channel state information (CSI)-based positioning pipelines rely ondeep neural networks (DNNs) in order to learn a mapping from estimated CSI toposition. Since real-world communication transceivers suffer from hardwareimpairments, CSI-based positioning systems typically rely on features that aredesigned by hand. In this paper, we propose a CSI-based positioning pipelinethat directly takes raw CSI measurements and learns features using a structuredDNN in order to generate probability maps describing the likelihood of thetransmitter being at pre-defined grid points. To further improve thepositioning accuracy of moving user equipments, we propose to fuse atime-series of learned CSI features or a time-series of probability maps. Todemonstrate the efficacy of our methods, we perform experiments with real-worldindoor line-of-sight (LoS) and non-LoS channel measurements. We show that CSIfeature learning and time-series fusion can reduce the mean distance error byup to 2.5$\boldsymbol\times$ compared to the state-of-the-art.

Feature Learning for Neural-Network-Based Positioning with Channel State Information