A Comparative Study of SINR Features in 5G Positioning with Machine Learning

Indoor positioning in 5G networks is crucial for enhancing location-based services. This paper explores the use of Reference Signal Received Power (RSRP) and Signal to Interference plus Noise Ratio (SINR) as features in machine learning models to improve indoor fingerprint positioning accuracy. This study highlights the potential of SINR as a valuable machine learning feature for accurate indoor 5G positioning.

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Publication Details

Published
2024-10-16
DOI
https://doi.org/10.1109/ictc62082.2024.10827263
Primary Topic
Indoor and Outdoor Localization Technologies
Type
article
Field-Weighted Citation Impact
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article

A Comparative Study of SINR Features in 5G Positioning with Machine Learning

Jee-Hyeon Na, Chang-Hyun Jung, Tae-Jung Kim, Kyu-hee Bak
Indoor and Outdoor Localization Technologies
article

A Comparative Study of SINR Features in 5G Positioning with Machine Learning

Jee-Hyeon Na, Chang-Hyun Jung, Tae-Jung Kim, Kyu-hee Bak
article en

Abstract

Indoor positioning in 5G networks is crucial for enhancing location-based services. This paper explores the use of Reference Signal Received Power (RSRP) and Signal to Interference plus Noise Ratio (SINR) as features in machine learning models to improve indoor fingerprint positioning accuracy. This study highlights the potential of SINR as a valuable machine learning feature for accurate indoor 5G positioning.

Electronics and Telecommunications Research Institute (KR)
Openalex Percentile: Top 76%
Indoor and Outdoor Localization Technologies
DOISourceOpenAlex
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A Comparative Study of SINR Features in 5G Positioning with Machine Learning — Jee-Hyeon Na, Chang-Hyun Jung, et al. · (2024) | TGRS Research Map | TGRS