Overview of Using Signaling Data from Radio Interface with Machine Learning Approaches
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Overview of Using Signaling Data from Radio Interface with Machine Learning Approaches
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2023 International Conference on Military Technologies (ICMT)
ISBN
979-8-3503-2568-3
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
1-8
Publisher name
IEEE Industrial Electronic Society
Place of publication
Vienna
Event location
Brno
Event date
May 23, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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