Subscriber Location in 5G mmWave Networks - Machine Learning RF Pattern Matching

Jativa E. Rene; Anthony Salazar; Katty Beltran; Oliver Caisaluisa

doi:10.1109/ROPEC55836.2022.10018626

Subscriber Location in 5G mmWave Networks - Machine Learning RF Pattern Matching

Jativa E. Rene
, Anthony Salazar
, Katty Beltran
, Oliver Caisaluisa

Ingeniería Electrónica y Automatización

Universidad San Francisco de Quito

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

3 Scopus citations

Abstract

A realistic simulated 5G DM-MIMO wireless network operating at 28 GHz mmWaves has been deployed using Open Street Maps and Matlab® over the campus of Universidad San Francisco de Quito (USFQ). Received Signal Strength fingerprints have been collected at Base Station antenna array, and the K-Nearest Neighbors method has been used to perform the match between the received RF patterns and the stored fingerprints. Three different procedures were tested and their results were compared, exhibiting very good outcomes in all the cases.

Original language	English
Title of host publication	2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665458924
DOIs	https://doi.org/10.1109/ROPEC55836.2022.10018626
State	Published - 2022
Event	2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022 - Ixtapa, Mexico Duration: 9 Nov 2022 → 11 Nov 2022

Publication series

Name	2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022

Conference

Conference	2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022
Country/Territory	Mexico
City	Ixtapa
Period	9/11/22 → 11/11/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

5G wireless networks
K-Nearest Neighbors
KNN
fingerprinting
mmWave
subscriber location

Access to Document

10.1109/ROPEC55836.2022.10018626

Cite this

Rene, J. E., Salazar, A., Beltran, K., & Caisaluisa, O. (2022). Subscriber Location in 5G mmWave Networks - Machine Learning RF Pattern Matching. In 2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022 (2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ROPEC55836.2022.10018626

Rene, Jativa E. ; Salazar, Anthony ; Beltran, Katty et al. / Subscriber Location in 5G mmWave Networks - Machine Learning RF Pattern Matching. 2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022).

@inproceedings{7cd3c683ed274ce996c0205617dcacad,

title = "Subscriber Location in 5G mmWave Networks - Machine Learning RF Pattern Matching",

abstract = "A realistic simulated 5G DM-MIMO wireless network operating at 28 GHz mmWaves has been deployed using Open Street Maps and Matlab{\textregistered} over the campus of Universidad San Francisco de Quito (USFQ). Received Signal Strength fingerprints have been collected at Base Station antenna array, and the K-Nearest Neighbors method has been used to perform the match between the received RF patterns and the stored fingerprints. Three different procedures were tested and their results were compared, exhibiting very good outcomes in all the cases.",

keywords = "5G wireless networks, K-Nearest Neighbors, KNN, fingerprinting, mmWave, subscriber location",

author = "Rene, \{Jativa E.\} and Anthony Salazar and Katty Beltran and Oliver Caisaluisa",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022 ; Conference date: 09-11-2022 Through 11-11-2022",

year = "2022",

doi = "10.1109/ROPEC55836.2022.10018626",

language = "Ingl{\'e}s",

series = "2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022",

}

Rene, JE, Salazar, A, Beltran, K & Caisaluisa, O 2022, Subscriber Location in 5G mmWave Networks - Machine Learning RF Pattern Matching. in 2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022. 2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022, Institute of Electrical and Electronics Engineers Inc., 2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022, Ixtapa, Mexico, 9/11/22. https://doi.org/10.1109/ROPEC55836.2022.10018626

Subscriber Location in 5G mmWave Networks - Machine Learning RF Pattern Matching. / Rene, Jativa E.; Salazar, Anthony; Beltran, Katty et al.
2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022).

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

TY - GEN

T1 - Subscriber Location in 5G mmWave Networks - Machine Learning RF Pattern Matching

AU - Rene, Jativa E.

AU - Salazar, Anthony

AU - Beltran, Katty

AU - Caisaluisa, Oliver

PY - 2022

Y1 - 2022

N2 - A realistic simulated 5G DM-MIMO wireless network operating at 28 GHz mmWaves has been deployed using Open Street Maps and Matlab® over the campus of Universidad San Francisco de Quito (USFQ). Received Signal Strength fingerprints have been collected at Base Station antenna array, and the K-Nearest Neighbors method has been used to perform the match between the received RF patterns and the stored fingerprints. Three different procedures were tested and their results were compared, exhibiting very good outcomes in all the cases.

AB - A realistic simulated 5G DM-MIMO wireless network operating at 28 GHz mmWaves has been deployed using Open Street Maps and Matlab® over the campus of Universidad San Francisco de Quito (USFQ). Received Signal Strength fingerprints have been collected at Base Station antenna array, and the K-Nearest Neighbors method has been used to perform the match between the received RF patterns and the stored fingerprints. Three different procedures were tested and their results were compared, exhibiting very good outcomes in all the cases.

KW - 5G wireless networks

KW - K-Nearest Neighbors

KW - KNN

KW - fingerprinting

KW - mmWave

KW - subscriber location

UR - https://www.scopus.com/pages/publications/85147539883

U2 - 10.1109/ROPEC55836.2022.10018626

DO - 10.1109/ROPEC55836.2022.10018626

M3 - Contribución a la conferencia

AN - SCOPUS:85147539883

T3 - 2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022

BT - 2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022

Y2 - 9 November 2022 through 11 November 2022

ER -

Rene JE, Salazar A, Beltran K, Caisaluisa O. Subscriber Location in 5G mmWave Networks - Machine Learning RF Pattern Matching. In 2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022). doi: 10.1109/ROPEC55836.2022.10018626

Subscriber Location in 5G mmWave Networks - Machine Learning RF Pattern Matching

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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