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Detail publikace
YOUNESIAN, E. HOŠEK, J.
Originální název
Towards 5G Networks Optimization through Machine Learning-aided Processing Large Datasets
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
This paper presents a novel approach for optimizing 5G networks through the use of machine learning techniques for processing large datasets. With the increasing demand for high-speed and low-latency communications, 5G networks have emerged as a promising solution. However, the optimization of these networks remains a challenge due to the complexity of the systems and the vast amount of data generated by them. To address this issue, we propose a framework that utilizes data preprocessing in time series data to analyze large datasets and extract useful information. It can be used to optimize various aspects of the 5G network. Specifically, we present results of implementing seasonal decomposition of time series which is one of the techniques in time series forecasting. This preprocessed data will be implemented to time series machine learning models to predict the future delay between routers and finally propose the best available route. Overall, our work presents a valuable contribution to the field of 5G network optimization and highlights the potential of machine learning techniques in this domain.
Klíčová slova
Machine learning, Time series forecasting, Data preprocessing, Beyond 5G networks, Network slicing
Autoři
YOUNESIAN, E.; HOŠEK, J.
Vydáno
25. 4. 2023
Nakladatel
Brno University of Technology, Faculty of Electrical Engineering and Communication
Místo
Brno
ISBN
978-80-214-6153-6
Kniha
Proceedings I of the 29th year Conference Student EEICT 2023 General papers
Edice
1
Strany od
369
Strany do
373
Strany počet
5
URL
https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2023_sbornik_1.pdf
BibTex
@inproceedings{BUT183352, author="Elham {Younesian} and Jiří {Hošek}", title="Towards 5G Networks Optimization through Machine Learning-aided Processing Large Datasets", booktitle="Proceedings I of the 29th year Conference Student EEICT 2023 General papers", year="2023", series="1", pages="369--373", publisher="Brno University of Technology, Faculty of Electrical Engineering and Communication", address="Brno", isbn="978-80-214-6153-6", url="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2023_sbornik_1.pdf" }