ANALYSIS OF BASIC TOPOLOGIES AND TYPICAL STRUCTURES OF TELECOMMUNICATION NETWORKS

This article analyzes the topology of telecommunication networks (TN) with various structures and reveals that, despite the numerous ways to connect nodes, network construction is based on three main (basic) topologies: bus, ring, and star. It is shown that in telecommunication networks, radial, ring, and radial-ring structures are among the most frequently used typical structures. The penalty function method determines the optimal TN structure based on the criterion of overall network reduced costs. Additionally, when choosing the most preferred basic typical structure for building telecommunication networks, the relative efficiency coefficient is proposed as an indicator.

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Publication Details

Journal
International journal of data science and machine learning.
Published
2024-08-15
DOI
https://doi.org/10.55640/ijdsml-04-02-03
Citations
1
Primary Topic
Advanced Wireless Network Optimization
Type
article
Field-Weighted Citation Impact
0.21
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article

ANALYSIS OF BASIC TOPOLOGIES AND TYPICAL STRUCTURES OF TELECOMMUNICATION NETWORKS

Saydiakhat S. Parsiyev
1 citations
International journal of data science and machine learning.
Advanced Wireless Network Optimization
0.21
article

ANALYSIS OF BASIC TOPOLOGIES AND TYPICAL STRUCTURES OF TELECOMMUNICATION NETWORKS

Saydiakhat S. Parsiyev
article en
1 citations

Abstract

This article analyzes the topology of telecommunication networks (TN) with various structures and reveals that, despite the numerous ways to connect nodes, network construction is based on three main (basic) topologies: bus, ring, and star. It is shown that in telecommunication networks, radial, ring, and radial-ring structures are among the most frequently used typical structures. The penalty function method determines the optimal TN structure based on the criterion of overall network reduced costs. Additionally, when choosing the most preferred basic typical structure for building telecommunication networks, the relative efficiency coefficient is proposed as an indicator.

International journal of data science and machine learning.Vol. 04(02)
National University of Uzbekistan (UZ)
Openalex Percentile: Top 47%
Advanced Wireless Network Optimization
0.21
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