AI-Based UE Positioning Using Inter-Frequency Measurements in 5G Networks

In this study, we deployed a multi-frequency environment with Sub-6GHz and millimeter wave (mmWave) base stations, closely resembling real-world conditions. Data were collected through inter-frequency measurements, and User Equipment (UE) positioning was performed using AI techniques. This research demonstrates the feasibility of UE positioning through inter-frequency cooperation in a multi-frequency 5G network environment.

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Publication Details

Published
2024-10-16
DOI
https://doi.org/10.1109/ictc62082.2024.10826850
Citations
1
Primary Topic
Millimeter-Wave Propagation and Modeling
Type
article
Field-Weighted Citation Impact
0.21
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AI-Based UE Positioning Using Inter-Frequency Measurements in 5G Networks

Jee-Hyeon Na, Chang-Hyun Jung, Jayanta Choi, Tae-Jung Kim et al.
1 citations
Millimeter-Wave Propagation and Modeling
0.21
article

AI-Based UE Positioning Using Inter-Frequency Measurements in 5G Networks

Jee-Hyeon Na, Chang-Hyun Jung, Jayanta Choi, Tae-Jung Kim, Kyu-Hee Bak
article en
1 citations

Abstract

In this study, we deployed a multi-frequency environment with Sub-6GHz and millimeter wave (mmWave) base stations, closely resembling real-world conditions. Data were collected through inter-frequency measurements, and User Equipment (UE) positioning was performed using AI techniques. This research demonstrates the feasibility of UE positioning through inter-frequency cooperation in a multi-frequency 5G network environment.

Electronics and Telecommunications Research Institute (KR)
Affordable and clean energy
Openalex Percentile: Top 44%
Millimeter-Wave Propagation and Modeling
0.21
DOISourceOpenAlex
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AI-Based UE Positioning Using Inter-Frequency Measurements in 5G Networks — Jee-Hyeon Na, Chang-Hyun Jung, et al. · (2024) | TGRS Research Map | TGRS