Radio Frequency Pattern Matching - Subscriber Location in 5G Networks

René Játiva E; Oliver Caisaluisa; Katty Beltrán; Martín Gavilánez

doi:10.1007/978-3-031-48415-5_9

Radio Frequency Pattern Matching - Subscriber Location in 5G Networks

René Játiva E^*
, Oliver Caisaluisa
, Katty Beltrán
, Martín Gavilánez

^*Corresponding author for this work

Ingeniería Electrónica y Automatización

Universidad San Francisco de Quito

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

Received Signal Strength measures have been collected at the Base Station antenna array of a wireless network operating at 28 GHz mmWaves, and virtually deployed using Open Street Maps and Matlab®. These radio frequency patterns imprinted by a geolocated subscriber transmitting along the campus, have been used to automatically discover the characteristics of the area of interest by using k-means clustering into the proposed unsupervised method. This technique has been integrated into supervised ML methods based on K-Nearest Neighbors, in order to provide an accurate estimation of the subscriber position by performing the match between the received RF patterns and the stored fingerprints. New results exhibit an improved accuracy over previous works based on supervised ML methods. Furthermore, the impact of the operation frequency band over positioning has been evaluated within the range of 3.7–30 GHz. Results show that accuracy degrades at lower frequencies and some mitigation methods are discussed.

Original language	English
Title of host publication	Applications of Computational Intelligence - 6th IEEE Colombian Conference, ColCACI 2023, Revised Selected Papers
Editors	Alvaro David Orjuela-Cañón, Jesus A Lopez, Julián David Arias-Londoño
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	122-137
Number of pages	16
ISBN (Print)	9783031484148
DOIs	https://doi.org/10.1007/978-3-031-48415-5_9
State	Published - 2024
Event	6th IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023 - Bogota, Colombia Duration: 26 Jul 2023 → 28 Jul 2023

Publication series

Name	Communications in Computer and Information Science
Volume	1865 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	6th IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023
Country/Territory	Colombia
City	Bogota
Period	26/07/23 → 28/07/23

Keywords

5G wireless networks
K-Means Clustering
RFPM
Radio Frequency Pattern Matching
fingerprinting
k-means
mmWave
subscriber location

Access to Document

10.1007/978-3-031-48415-5_9

Cite this

Játiva E, R., Caisaluisa, O., Beltrán, K., & Gavilánez, M. (2024). Radio Frequency Pattern Matching - Subscriber Location in 5G Networks. In A. D. Orjuela-Cañón, J. A. Lopez, & J. D. Arias-Londoño (Eds.), Applications of Computational Intelligence - 6th IEEE Colombian Conference, ColCACI 2023, Revised Selected Papers (pp. 122-137). (Communications in Computer and Information Science; Vol. 1865 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-48415-5_9

Játiva E, René ; Caisaluisa, Oliver ; Beltrán, Katty et al. / Radio Frequency Pattern Matching - Subscriber Location in 5G Networks. Applications of Computational Intelligence - 6th IEEE Colombian Conference, ColCACI 2023, Revised Selected Papers. editor / Alvaro David Orjuela-Cañón ; Jesus A Lopez ; Julián David Arias-Londoño. Springer Science and Business Media Deutschland GmbH, 2024. pp. 122-137 (Communications in Computer and Information Science).

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Játiva E, R, Caisaluisa, O, Beltrán, K & Gavilánez, M 2024, Radio Frequency Pattern Matching - Subscriber Location in 5G Networks. in AD Orjuela-Cañón, JA Lopez & JD Arias-Londoño (eds), Applications of Computational Intelligence - 6th IEEE Colombian Conference, ColCACI 2023, Revised Selected Papers. Communications in Computer and Information Science, vol. 1865 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 122-137, 6th IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023, Bogota, Colombia, 26/07/23. https://doi.org/10.1007/978-3-031-48415-5_9

Radio Frequency Pattern Matching - Subscriber Location in 5G Networks. / Játiva E, René; Caisaluisa, Oliver; Beltrán, Katty et al.
Applications of Computational Intelligence - 6th IEEE Colombian Conference, ColCACI 2023, Revised Selected Papers. ed. / Alvaro David Orjuela-Cañón; Jesus A Lopez; Julián David Arias-Londoño. Springer Science and Business Media Deutschland GmbH, 2024. p. 122-137 (Communications in Computer and Information Science; Vol. 1865 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Radio Frequency Pattern Matching - Subscriber Location in 5G Networks

AU - Játiva E, René

AU - Caisaluisa, Oliver

AU - Beltrán, Katty

AU - Gavilánez, Martín

PY - 2024

Y1 - 2024

N2 - Received Signal Strength measures have been collected at the Base Station antenna array of a wireless network operating at 28 GHz mmWaves, and virtually deployed using Open Street Maps and Matlab®. These radio frequency patterns imprinted by a geolocated subscriber transmitting along the campus, have been used to automatically discover the characteristics of the area of interest by using k-means clustering into the proposed unsupervised method. This technique has been integrated into supervised ML methods based on K-Nearest Neighbors, in order to provide an accurate estimation of the subscriber position by performing the match between the received RF patterns and the stored fingerprints. New results exhibit an improved accuracy over previous works based on supervised ML methods. Furthermore, the impact of the operation frequency band over positioning has been evaluated within the range of 3.7–30 GHz. Results show that accuracy degrades at lower frequencies and some mitigation methods are discussed.

AB - Received Signal Strength measures have been collected at the Base Station antenna array of a wireless network operating at 28 GHz mmWaves, and virtually deployed using Open Street Maps and Matlab®. These radio frequency patterns imprinted by a geolocated subscriber transmitting along the campus, have been used to automatically discover the characteristics of the area of interest by using k-means clustering into the proposed unsupervised method. This technique has been integrated into supervised ML methods based on K-Nearest Neighbors, in order to provide an accurate estimation of the subscriber position by performing the match between the received RF patterns and the stored fingerprints. New results exhibit an improved accuracy over previous works based on supervised ML methods. Furthermore, the impact of the operation frequency band over positioning has been evaluated within the range of 3.7–30 GHz. Results show that accuracy degrades at lower frequencies and some mitigation methods are discussed.

KW - 5G wireless networks

KW - K-Means Clustering

KW - RFPM

KW - Radio Frequency Pattern Matching

KW - fingerprinting

KW - k-means

KW - mmWave

KW - subscriber location

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DO - 10.1007/978-3-031-48415-5_9

M3 - Contribución a la conferencia

AN - SCOPUS:85177861076

SN - 9783031484148

T3 - Communications in Computer and Information Science

SP - 122

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BT - Applications of Computational Intelligence - 6th IEEE Colombian Conference, ColCACI 2023, Revised Selected Papers

A2 - Orjuela-Cañón, Alvaro David

A2 - Lopez, Jesus A

A2 - Arias-Londoño, Julián David

PB - Springer Science and Business Media Deutschland GmbH

T2 - 6th IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2023

Y2 - 26 July 2023 through 28 July 2023

ER -

Játiva E R, Caisaluisa O, Beltrán K, Gavilánez M. Radio Frequency Pattern Matching - Subscriber Location in 5G Networks. In Orjuela-Cañón AD, Lopez JA, Arias-Londoño JD, editors, Applications of Computational Intelligence - 6th IEEE Colombian Conference, ColCACI 2023, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2024. p. 122-137. (Communications in Computer and Information Science). doi: 10.1007/978-3-031-48415-5_9