Serbian Journal of Electrical Engineering
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2021, vol. 18, br. 3, str. 397-426
Application of deep learning algorithms and architectures in the new generation of mobile networks
(naslov ne postoji na srpskom)
aVlatacom Institute, Belgrade + Singidunum University, Belgrade + Universidade Lusófona de Humanidades e Tecnologias, COPELABS, Lisbon, Portugal
bVlatacom Institute, Belgrade
cVlatacom Institute, Belgrade + College of Applied Technical Sciences, Kruševac
dVlatacom Institute, Belgrade + Univerzitet Singidunum, Beograd
eUniversidade de Lisboa, Instituto Superior Técnico, Instituto de Telecomunicações, Lisbon, Portugal

e-adresadejan.dasic@vlatacom.com, miljan.vucetic@vlatacom.com, nilic@asss.edu.rs, milos.stankovic@singidunum.ac.rs, beko.marko@gmail.com
This work was supported by Vlatacom Institute and in part by the Science Fund of the Republic of Serbia under Grant 6524745, AI-DECIDE

Ključne reči: Deep learning; Mobile networks; Mobile data analysis; Network security; Drone-based communications; Signal processing; Modulation Classification
(ne postoji na srpskom)
Operators of modern mobile networks are faced with significant challenges in providing the requested level of service to an ever increasing number of user entities. Advanced machine learning techniques based on deep architectures and appropriate learning methods are recognized as promising ways of tackling the said challenges in many aspects of mobile networks, such as mobile data and mobility analysis, network control, network security and signal processing. Having firstly presented the background of deep learning and related technologies, the paper goes on to present the architectures used for deployment of deep learning in mobile networks. The paper continues with an overview of applications and services related to the new generation of mobile networks that employ deep learning methods. Finally, the paper presents practical use case of modulation classification as implementation of deep learning in an application essential for modern spectrum management. We complete this work by pinpointing future directions for research.
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O članku

jezik rada: engleski
vrsta rada: neklasifikovan
DOI: 10.2298/SJEE2103397D
objavljen u SCIndeksu: 10.12.2021.
Creative Commons License 4.0

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