User-Side Indoor Localization Using CSI Fingerprinting

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We consider a scalable User Equipment (UE)-side indoor localization framework that processes Channel State Information (CSI) from multiple Access Points (APs). We use CSI features that are resilient to synchronization errors and other hardware impairments. As a consequence our method does not require accurate network synchronization among APs. Increasing the number of APs considered by a UE profoundly improves fingerprint positioning, with the cost of increasing complexity and channel estimation time. In order to improve scalability of the framework to large networks consisting of multiple APs in many rooms, we train a multi-layer neural network that combines CSI features and unique AP identifiers of a subset of APs in range of a UE. We simulate UE-side localization using CSI obtained from a commercial raytracer. The considered method processing frequency selective CSI achieves an average positioning error of 60cm, outperforming methods that process received signal strength information only. The mean localization accuracy loss compared to a non-scalable approach with perfect synchronization and CSI is 20cm.

Original languageEnglish
Title of host publication2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication, SPAWC 2022
Number of pages5
ISBN (Electronic)978-1-6654-9455-7
Publication statusPublished - 2022
MoE publication typeA4 Conference publication
EventIEEE International Workshop on Signal Processing Advances in Wireless Communication - Oulu, Finland
Duration: 4 Jul 20226 Jul 2022

Publication series

ISSN (Electronic)1948-3252


ConferenceIEEE International Workshop on Signal Processing Advances in Wireless Communication
Abbreviated titleSPAWC


  • Channel state information
  • fingerprinting
  • neural networks
  • user equipment (UE)-side indoor localization


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