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

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

2 Citas (Scopus)

Resumen

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.

Idioma original	Inglés
Título de la publicación alojada	2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022
Editorial	Institute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)	9781665458924
DOI	https://doi.org/10.1109/ROPEC55836.2022.10018626
Estado	Publicada - 2022
Evento	2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022 - Ixtapa, México Duración: 9 nov. 2022 → 11 nov. 2022

Serie de la publicación

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

Conferencia

Conferencia	2022 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2022
País/Territorio	México
Ciudad	Ixtapa
Período	9/11/22 → 11/11/22

ODS de las Naciones Unidas

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10.1109/ROPEC55836.2022.10018626

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Rene, J. E., Salazar, A., Beltran, K., & Caisaluisa, O. (2022). Subscriber Location in 5G mmWave Networks - Machine Learning RF Pattern Matching. En 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. En 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, México, 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).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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

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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. En 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

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