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Fingerprinting-based Indoor Localization in a 3x3 Meter Grid Using OFDM Signals at Sub-6 GHz
  • +4
  • Jaspreet Kaur,
  • Kang Tan,
  • Muhammad Khan Z,
  • Olaoluwa Popoola R,
  • Muhammad Ali Imran,
  • Qammer H.Abbasi,
  • Hasan T.Abbas
Jaspreet Kaur
University of Glasgow James Watt School of Engineering

Corresponding Author:[email protected]

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Kang Tan
University of Glasgow James Watt School of Engineering
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Muhammad Khan Z
University of Glasgow James Watt School of Engineering
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Olaoluwa Popoola R
University of Glasgow James Watt School of Engineering
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Muhammad Ali Imran
University of Glasgow James Watt School of Engineering
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Qammer H.Abbasi
University of Glasgow James Watt School of Engineering
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Hasan T.Abbas
University of Glasgow James Watt School of Engineering
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Abstract

Accurately determining the indoor location of mobile devices has garnered great interest due to its significant challenge in locating sources due to non-line-of-sight propagation and multipath effects. To address this challenge, This paper proposes a new approach to indoor positioning that utilises channel state information (CSI) and machine learning (ML) techniques to improve Accuracy. The proposed method extracts subcarrier amplitude and phase differences from CSI data to create fingerprints, which are then clustered to identify the number of groups of data and split into two sub-databases using a threshold. The ML algorithms and network architecture are used to train both sub-databases of fingerprints. Experiments conducted in a standard indoor environment demonstrate the effectiveness of the proposed method.
21 Feb 2024Submitted to Applied AI Letters
23 Feb 2024Submission Checks Completed
23 Feb 2024Assigned to Editor
24 Jul 2024Review(s) Completed, Editorial Evaluation Pending
25 Jul 2024Editorial Decision: Revise Minor
06 Aug 20241st Revision Received
09 Aug 2024Submission Checks Completed
09 Aug 2024Assigned to Editor
27 Aug 2024Reviewer(s) Assigned
02 Sep 2024Review(s) Completed, Editorial Evaluation Pending
02 Sep 2024Editorial Decision: Revise Minor
06 Sep 20242nd Revision Received
17 Sep 2024Submission Checks Completed
17 Sep 2024Assigned to Editor
17 Sep 2024Review(s) Completed, Editorial Evaluation Pending
21 Sep 2024Editorial Decision: Accept