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

Emre Gönültaş; Sueda Taner; Howard Huang; Christoph Studer

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arXiv · 2110.15160

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

Emre Gönültaş·Sueda Taner·Howard Huang·Christoph Studer

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Abstract

Recent channel state information (CSI)-based positioning pipelines rely on deep neural networks (DNNs) in order to learn a mapping from estimated CSI to position. Since real-world communication transceivers suffer from hardware impairments, CSI-based positioning systems typically rely on features that are designed by hand. In this paper, we propose a CSI-based positioning pipeline that directly takes raw CSI measurements and learns features using a structured DNN in order to generate probability maps describing the likelihood of the transmitter being at pre-defined grid points. To further improve the positioning accuracy of moving user equipments, we propose to fuse a time-series of learned CSI features or a time-series of probability maps. To demonstrate the efficacy of our methods, we perform experiments with real-world indoor line-of-sight (LoS) and non-LoS channel measurements. We show that CSI feature learning and time-series fusion can reduce the mean distance error by up to 2.5$\boldsymbol\times$ compared to the state-of-the-art.

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Emre Gönültaş, Sueda Taner, Howard Huang, Christoph Studer. 2021-10-28. Feature Learning for Neural-Network-Based Positioning with Channel State Information. https://arxiv.org/abs/2110.15160

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