User-Side Indoor Localization Using CSI Fingerprinting

Parham Kazemi; Hanan Al-Tous; Christoph Studer; Olav Tirkkonen

doi:10.1109/SPAWC51304.2022.9833973

User-Side Indoor Localization Using CSI Fingerprinting

Parham Kazemi, Hanan Al-Tous, Christoph Studer, Olav Tirkkonen

Communications Theory

Swiss Federal Institute of Technology Zurich

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

1 Citation (Scopus)

88 Downloads (Pure)

Abstract

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 language	English
Title of host publication	2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication, SPAWC 2022
Publisher	IEEE
Number of pages	5
ISBN (Electronic)	978-1-6654-9455-7
DOIs	https://doi.org/10.1109/SPAWC51304.2022.9833973
Publication status	Published - 2022
MoE publication type	A4 Conference publication
Event	IEEE International Workshop on Signal Processing Advances in Wireless Communication - Oulu, Finland Duration: 4 Jul 2022 → 6 Jul 2022

Publication series

Name	SPAWC
Volume	2022-July
ISSN (Electronic)	1948-3252

Conference

Conference	IEEE International Workshop on Signal Processing Advances in Wireless Communication
Abbreviated title	SPAWC
Country/Territory	Finland
City	Oulu
Period	04/07/2022 → 06/07/2022

Keywords

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

Access to Document

10.1109/SPAWC51304.2022.9833973

Kazemi_User-SideAccepted author manuscript, 528 KB

Cite this

@inproceedings{290d4842b1d54666a0f33d1699ba2260,

title = "User-Side Indoor Localization Using CSI Fingerprinting",

abstract = "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. ",

keywords = "Channel state information, fingerprinting, neural networks, user equipment (UE)-side indoor localization",

author = "Parham Kazemi and Hanan Al-Tous and Christoph Studer and Olav Tirkkonen",

note = "Funding Information: This work was funded in part by the European Union under the framework of the project H2020-MSCA-ITN 813999 Windmill and the Academy of Finland (grant 319484). Publisher Copyright: {\textcopyright} 2022 IEEE. | openaire: EC/H2020/813999/EU//WINDMILL; IEEE International Workshop on Signal Processing Advances in Wireless Communication, SPAWC ; Conference date: 04-07-2022 Through 06-07-2022",

year = "2022",

doi = "10.1109/SPAWC51304.2022.9833973",

language = "English",

series = "SPAWC",

publisher = "IEEE",

booktitle = "2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication, SPAWC 2022",

address = "United States",

}

Kazemi, P, Al-Tous, H, Studer, C & Tirkkonen, O 2022, User-Side Indoor Localization Using CSI Fingerprinting. in 2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication, SPAWC 2022. SPAWC, vol. 2022-July, IEEE, IEEE International Workshop on Signal Processing Advances in Wireless Communication, Oulu, Finland, 04/07/2022. https://doi.org/10.1109/SPAWC51304.2022.9833973

User-Side Indoor Localization Using CSI Fingerprinting. / Kazemi, Parham; Al-Tous, Hanan; Studer, Christoph et al.
2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication, SPAWC 2022. IEEE, 2022. (SPAWC; Vol. 2022-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - User-Side Indoor Localization Using CSI Fingerprinting

AU - Kazemi, Parham

AU - Al-Tous, Hanan

AU - Studer, Christoph

AU - Tirkkonen, Olav

N1 - Funding Information: This work was funded in part by the European Union under the framework of the project H2020-MSCA-ITN 813999 Windmill and the Academy of Finland (grant 319484). Publisher Copyright: © 2022 IEEE. | openaire: EC/H2020/813999/EU//WINDMILL

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - Channel state information

KW - fingerprinting

KW - neural networks

KW - user equipment (UE)-side indoor localization

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M3 - Conference article in proceedings

AN - SCOPUS:85136019649

T3 - SPAWC

BT - 2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication, SPAWC 2022

PB - IEEE

T2 - IEEE International Workshop on Signal Processing Advances in Wireless Communication

Y2 - 4 July 2022 through 6 July 2022

ER -

User-Side Indoor Localization Using CSI Fingerprinting

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

WINDMILL: Integrating Wireless Communication ENgineering and MachIne Learning

-: Radio Network Optimization for Heterogeneous Machine Connectivity

Science-IT

Cite this

User-Side Indoor Localization Using CSI Fingerprinting

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Projects

WINDMILL: Integrating Wireless Communication ENgineering and MachIne Learning

-: Radio Network Optimization for Heterogeneous Machine Connectivity

Equipment

Science-IT

Cite this