OVERVIEW STUDY OF MOBILE NETWORK TRAFFIC FOR BTS STATIONS
In recent years, Machine Learning (ML) has become a crucial and promising tool for forecasting and solving a wide range of complex problems. The rapid development of machine learning is closely linked to technological advancements and has also driven the growth of the AI community and open- sou...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
Trường Đại học Vinh
2024-12-01
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| Series: | Tạp chí Khoa học |
| Subjects: | |
| Online Access: | https://vujs.vn//api/view.aspx?cid=93f7b4a1-4e40-4ce9-a763-21b553ba752e |
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| Summary: | In recent years, Machine Learning (ML) has become a crucial
and promising tool for forecasting and solving a wide range of
complex problems. The rapid development of machine
learning is closely linked to technological advancements and
has also driven the growth of the AI community and open-
source tools (e.g., TensorFlow, Keras, PyTorch, fast.ai). This
enables researchers to deploy and apply machine learning
algorithms more effectively. This paper provides an overview
of mobile network traffic at BTS stations, conducted from a
data-driven perspective, focusing on extracting and
transforming data into information that serves production and
business purposes within mobile networks, as well as
describing the characteristics of user traffic. The authors used
the Google Colab environment to analyze network time
statistics to determine traffic in each area. Leveraging large
volumes of information helps improve mobile network
performance and address various issues (e.g., anomaly
detection) that may impact network infrastructure. The study's
findings contribute to addressing certain practical challenges
in deployment, optimization, resource allocation, and energy
savings for mobile networks. |
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| ISSN: | 1859-2228 |