Overview of Using Signaling Data from Radio Interface with Machine Learning Approaches

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00368057" target="_blank" >RIV/68407700:21230/23:00368057 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/ICMT58149.2023.10171266" target="_blank" >https://doi.org/10.1109/ICMT58149.2023.10171266</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICMT58149.2023.10171266" target="_blank" >10.1109/ICMT58149.2023.10171266</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Overview of Using Signaling Data from Radio Interface with Machine Learning Approaches

Popis výsledku v původním jazyce

Mobile networks technologies are evolving rapidly in parallel with smart mobile devices wide spreading. On other hand, utilization of Artificial Intelligence in mobile networks has been increasing widely. It starts from mobile phones applications to mobile network operations, planning, optimization, etc. In this paper, an overview of using signalling data from radio interface in cooperation with machine learning techniques is introduced. The main machine learning types and models are summarized, as well as some of previous related works mainly depended on applying Machine learning on radio signalling. Benefits of those Machine learning-Signaling combinations vary from enhancing network key performance indicators to predicting user's specifications as trajectory, location, work, gender, etc. Moreover, mobile network planning, coverage evaluation, path loss prediction and channel modeling can be enhanced by using machine learning.

Název v anglickém jazyce

Overview of Using Signaling Data from Radio Interface with Machine Learning Approaches

Popis výsledku anglicky

Mobile networks technologies are evolving rapidly in parallel with smart mobile devices wide spreading. On other hand, utilization of Artificial Intelligence in mobile networks has been increasing widely. It starts from mobile phones applications to mobile network operations, planning, optimization, etc. In this paper, an overview of using signalling data from radio interface in cooperation with machine learning techniques is introduced. The main machine learning types and models are summarized, as well as some of previous related works mainly depended on applying Machine learning on radio signalling. Benefits of those Machine learning-Signaling combinations vary from enhancing network key performance indicators to predicting user's specifications as trajectory, location, work, gender, etc. Moreover, mobile network planning, coverage evaluation, path loss prediction and channel modeling can be enhanced by using machine learning.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

2023 International Conference on Military Technologies (ICMT)

ISBN

979-8-3503-2568-3

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

1-8

Název nakladatele

IEEE Industrial Electronic Society

Místo vydání

Vienna

Místo konání akce

Brno

Datum konání akce

23. 5. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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