Interpolation-Based Densification of Sparse Measurement Datasets for 5G+ Systems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F24%3APU151829" target="_blank" >RIV/00216305:26220/24:PU151829 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10605926" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10605926</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TSP63128.2024.10605926" target="_blank" >10.1109/TSP63128.2024.10605926</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Interpolation-Based Densification of Sparse Measurement Datasets for 5G+ Systems

Popis výsledku v původním jazyce

The process of obtaining dense datasets from heterogeneous cellular networks is a difficult task that requires extensive measurement campaigns, considerable time, and financial resources. Therefore, we propose an interpolation-based approach to densify sparse datasets acquired from a limited number of measurements. We demonstrate that the proposed approach can accurately model the signal levels of the serving base station (BS) and predict the coverage by neighboring cells. With the best performing linear and Kriging interpolation algorithms, we achieved values of mean absolute error under 3 dB. Specifically, both methods achieved a prediction error of less than 5 dB for more than 80 % of the measurement points. The prediction accuracy was nearly constant over the entire range of separation distances between the BSs and end devices. Owing to the minimal need for parameterization of the input dataset, the proposed solution is significantly less computationally demanding than machine learning (ML)-based approaches, and does not require training-validation cycles.

Název v anglickém jazyce

Interpolation-Based Densification of Sparse Measurement Datasets for 5G+ Systems

Popis výsledku anglicky

The process of obtaining dense datasets from heterogeneous cellular networks is a difficult task that requires extensive measurement campaigns, considerable time, and financial resources. Therefore, we propose an interpolation-based approach to densify sparse datasets acquired from a limited number of measurements. We demonstrate that the proposed approach can accurately model the signal levels of the serving base station (BS) and predict the coverage by neighboring cells. With the best performing linear and Kriging interpolation algorithms, we achieved values of mean absolute error under 3 dB. Specifically, both methods achieved a prediction error of less than 5 dB for more than 80 % of the measurement points. The prediction accuracy was nearly constant over the entire range of separation distances between the BSs and end devices. Owing to the minimal need for parameterization of the input dataset, the proposed solution is significantly less computationally demanding than machine learning (ML)-based approaches, and does not require training-validation cycles.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20203 - Telecommunications

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2024 47th International Conference on Telecommunications and Signal Processing (TSP)

ISBN

979-8-3503-6559-7

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

264-269

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

Online

Místo konání akce

Prague

Datum konání akce

10. 7. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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