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Title: Machine Learning-Based Small Cell Location Selection Process, Journal of Telecommunications and Information Technology, 2021, nr 2

Creator:

Kułacz, Łukasz ; Wasilewska, Małgorzata

Date:

2021, nr 2

Resource Type:

artykuł

Description:

In this paper, the authors present an algorithm for determining the location of wireless network small cells in a dense urban environment. This algorithm uses machine learning, such as k-means clustering and spectral clustering, as well as a very accurate propagation channel created using the ray tracing method. The authors compared two approaches to the small cell location selection process – one based on the assumption that end terminals may be arbitrarily assigned to stations, and the other assuming that the assignment is based on the received signal power. The mean bitrate values are derived for comparing different scenarios. The results show an improvement compared with the baseline results. This paper concludes that machine learning algorithms may be useful in terms of small cell location selection and also for allocating users to small cell base stations

Publisher:

National Institute of Telecommunications

Format:

application/pdf

Resource Identifier:

ISSN 1509-4553, on-line: ISSN 1899-8852 ; oai:bc.itl.waw.pl:2187

Source:

Journal of Telecommunications and Information Technology

Language:

ang

Rights Management:

Biblioteka Naukowa Instytutu Łączności

Subject and keywords:

base station selection k-means clustering user equipment allocation spectra clustering

Object collections:

Last modified:

Jul 13, 2021

In our library since:

Jul 13, 2021

Number of object content hits:

36

All available object's versions:

https://ribes-54.man.poznan.pl/publication/2487

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Edition name Date
Wasilewska, Małgorzata; Kułacz, Łukasz, Machine Learning-Based Small Cell Location Selection Process, Journal of Telecommunications and Information Technology, 2021, nr 2 Jul 13, 2021

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