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Title: Feature Learning for Neural-Network-Based Positioning with Channel State Information
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 nonLoS channel measurements. We show that CSI feature learning and time-series fusion can reduce the mean distance error by up to 2.5× compared to the state-of-the-art.  more » « less
Award ID(s):
1824379 1717559
NSF-PAR ID:
10381460
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Asilomar Conference on Signals, Systems, and Computers
Page Range / eLocation ID:
156 to 161
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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