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master's thesis
Analiza usluga u mobilnim mrežama 5G temeljena na metodama strojnog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. urn:nbn:hr:168:676387
Serdarušić, Filip
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Serdarušić, F. (2023). Analiza usluga u mobilnim mrežama 5G temeljena na metodama strojnog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:676387

Serdarušić, Filip. "Analiza usluga u mobilnim mrežama 5G temeljena na metodama strojnog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:676387

Serdarušić, Filip. "Analiza usluga u mobilnim mrežama 5G temeljena na metodama strojnog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:676387

Serdarušić, F. (2023). 'Analiza usluga u mobilnim mrežama 5G temeljena na metodama strojnog učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 18 November 2024, https://urn.nsk.hr/urn:nbn:hr:168:676387

Serdarušić F. Analiza usluga u mobilnim mrežama 5G temeljena na metodama strojnog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2023 [cited 2024 November 18] Available at: https://urn.nsk.hr/urn:nbn:hr:168:676387

F. Serdarušić, "Analiza usluga u mobilnim mrežama 5G temeljena na metodama strojnog učenja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:168:676387

Metadata
TitleAnaliza usluga u mobilnim mrežama 5G temeljena na metodama strojnog učenja
Title (english)Analysis of services in 5G mobile networks based on machine learning methods
AuthorFilip Serdarušić
MentorŽeljko Ilić (mentor)
Committee memberŽeljko Ilić (predsjednik povjerenstva)
Committee memberGordan Šišul (član povjerenstva)
Committee memberAdrian Satja Kurdija (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2023-02-10, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Dolaskom mobilne mreže 5G ostvario se značajan napredak u broju raspoloživih usluga, njihovoj kvaliteti te generalnog upravljanja mrežom i resursima. Danas se čini kako sve nova dostignuća u 5G idu u jednom od dva smjera: inovacije u kontekstu kvalitete usluge ili inovacije u području upravljanja mrežom. S obziom da se broj uređaja u mreži i količina generiranog prometa rapidno povećava, javlja se sve veća potreba za dobro razvijenim sustavom upravljanja mrežom i resursima. Jedan od načina postizanja toga je dinamička identifikacija obrasca uporabe analizom zahtjeva na kvalitetu usluge kojom bi se omogućila ispravna raspodijelu usluga i resursa. U ovako formuliranim problemima modeli strojnog i dubokog učenja pokazali se kao vrlo efektivno rješenje. U ovom Radu objašnjena je mobilna mreža 5G, motivacija za korištenje inteligentnih riješenja te zatim primjena metoda strojnog učenja u kontekstu klasifikacije usluga 5G.
Abstract (english)
With the advent of the 5G mobile network, significant progress has been made in the number of available services, their quality and general network and resource management. Today, it seems that all new developments in 5G are going in one of two directions: innovation in the context of service quality or innovation in the field of network management. With the number of devices in the network and the amount of traffic generated rapidly increasing, there is a growing need for a well-developed network and resource management system. One way to achieve this is to dynamically identify the pattern of use by analyzing service quality requirements that would allow for the correct allocation of services and resources. In the problems formulated in this way, the models of machine and deep learning proved to be a very effective solution. This paper explains the 5G mobile network, the motivation to use intelligent solutions and then the application of machine learning methods in the context of the classification of 5G services.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:676387
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-07-13 10:32:05