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Sökning: WFRF:(Caso Giuseppe) > (2024) > Empirical performan...

Empirical performance analysis and ML-based modeling of 5G non-standalone networks

Kousias, Konstantinos (författare)
University of Oslo, Norway
Rajiullah, Mohammad, 1981- (författare)
Karlstads universitet,Institutionen för matematik och datavetenskap (from 2013)
Caso, Giuseppe (författare)
Karlstads universitet,Institutionen för matematik och datavetenskap (from 2013)
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Alay, Özgü (författare)
Karlstads universitet,Institutionen för matematik och datavetenskap (from 2013),University of Oslo, Norway
Brunstrom, Anna, 1967- (författare)
Karlstads universitet,Institutionen för matematik och datavetenskap (from 2013)
Ali, Usman (författare)
Sapienza University of Rome, Italy
De Nardis, Luca (författare)
Sapienza University of Rome, Italy
Neri, Marco (författare)
Rohde & Schwarz, Italy
Di Benedetto, Maria-Gabriella (författare)
Sapienza University of Rome, Italy
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 (creator_code:org_t)
Elsevier, 2024
2024
Engelska.
Ingår i: Computer Networks. - : Elsevier. - 1389-1286 .- 1872-7069. ; 241
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • Fifth Generation (5G) networks are becoming the norm in the global telecommunications industry, and Mobile Network Operators (MNOs) are currently deploying 5G alongside their existing Fourth Generation (4G) networks. In this paper, we present results and insights from our large-scale measurement study on commercial 5G Non Standalone (NSA) deployments in a European country. We leverage the collected dataset, which covers two MNOs in Rome, Italy, to study network deployment and radio coverage aspects, and explore the performance of two use cases related to enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communication (URLLC). We further leverage a machine learning (ML)-based approach to model the Dual Connectivity (DC) feature enabled by 5G NSA. Our data-driven analysis shows that 5G NSA can provide higher downlink throughput and slightly lower latency compared to 4G. However, performance is influenced by several factors, including propagation conditions, system configurations, and handovers, ultimately highlighting the need for further system optimization. Moreover, by casting the DC modeling problem into a classification problem, we compare four supervised ML algorithms and show that a high model accuracy (up to 99%) can be achieved, in particular, when several radio coverage indicators from both access networks are used as input. Finally, we conduct analyses towards aiding the explainability of the ML models. 

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Telekommunikation (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Telecommunications (hsv//eng)
TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Kommunikationssystem (hsv//swe)
ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Communication Systems (hsv//eng)

Nyckelord

5G mobile communication systems
Machine learning
Telecommunication industry
5g non standalone
Empirical performance analysis
Fourth-generation (4G) networks
Global telecommunication
Learning Based Models
Machine-learning
Mobile network operators
Performance
Radio coverage
Telecommunications industry
Wireless networks
Computer Science
Datavetenskap

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