A Machine Learning Approach in 5G User Prediction

Upadhyay, Deepak; Tiwari, Pallavi; Mohd, Noor; Pant, Bhaskar

doi:10.1007/978-981-19-3571-8_59

Cited by 6 publications

(1 citation statement)

References 4 publications

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“…The main term and central nexus of the network is that of "fifth generation mobile communications" or 5G networks; however, there is already specific talk about 6G networks because 4G networks are currently still in transition. LTE is still working on the automation of 5G networks [75]. Thus, automatic learning algorithms have been implemented to reduce latency in the network, and these algorithms for 5G mainly use different deep learning techniques for network optimization, as seen in the networks formed [76].…”

Section: Resultsmentioning

confidence: 99%

Research Trends in the Use of Machine Learning Applied in Mobile Networks: A Bibliometric Approach and Research Agenda

García-Pineda,

Valencia-Arias,

Patiño-Vanegas

et al. 2023

Informatics

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This article aims to examine the research trends in the development of mobile networks from machine learning. The methodological approach starts from an analysis of 260 academic documents selected from the Scopus and Web of Science databases and is based on the parameters of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Quantity, quality and structure indicators are calculated in order to contextualize the documents’ thematic evolution. The results reveal that, in relation to the publications by country, the United States and China, who are competing for fifth generation (5G) network coverage and are responsible for manufacturing devices for mobile networks, stand out. Most of the research on the subject focuses on the optimization of resources and traffic to guarantee the best management and availability of a network due to the high demand for resources and greater amount of traffic generated by the many Internet of Things (IoT) devices that are being developed for the market. It is concluded that thematic trends focus on generating algorithms for recognizing and learning the data in the network and on trained models that draw from the available data to improve the experience of connecting to mobile networks.

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Section: Resultsmentioning

confidence: 99%