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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| Format: | Article |
| Language: | English |
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Trường Đại học Vinh
2024-12-01
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| Series: | Tạp chí Khoa học |
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| Online Access: | https://vujs.vn//api/view.aspx?cid=93f7b4a1-4e40-4ce9-a763-21b553ba752e |
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| _version_ | 1841550815107481600 |
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| author | Hoang Van Thuc*, Pham Van Ngoc, Doan Thi Thanh Thao, Vu Chien Thang, Pham Thanh Nam, Mac Thi Phuong |
| author_facet | Hoang Van Thuc*, Pham Van Ngoc, Doan Thi Thanh Thao, Vu Chien Thang, Pham Thanh Nam, Mac Thi Phuong |
| author_sort | Hoang Van Thuc*, Pham Van Ngoc, Doan Thi Thanh Thao, Vu Chien Thang, Pham Thanh Nam, Mac Thi Phuong |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-fe971d470e524261a3061aa2edc2d1c0 |
| institution | Kabale University |
| issn | 1859-2228 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Trường Đại học Vinh |
| record_format | Article |
| series | Tạp chí Khoa học |
| spelling | doaj-art-fe971d470e524261a3061aa2edc2d1c02025-01-10T03:56:53ZengTrường Đại học VinhTạp chí Khoa học1859-22282024-12-01534A51410.56824/vujs.2024a076aOVERVIEW STUDY OF MOBILE NETWORK TRAFFIC FOR BTS STATIONSHoang Van Thuc*, Pham Van Ngoc, Doan Thi Thanh Thao, Vu Chien Thang, Pham Thanh Nam, Mac Thi Phuong0University of Information and Communication Technology, Thai Nguyen University, VietnamIn 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.https://vujs.vn//api/view.aspx?cid=93f7b4a1-4e40-4ce9-a763-21b553ba752e5g trafficbase transceiver station5g bts5g traffic5g/bts traffic |
| spellingShingle | Hoang Van Thuc*, Pham Van Ngoc, Doan Thi Thanh Thao, Vu Chien Thang, Pham Thanh Nam, Mac Thi Phuong OVERVIEW STUDY OF MOBILE NETWORK TRAFFIC FOR BTS STATIONS Tạp chí Khoa học 5g traffic base transceiver station 5g bts 5g traffic 5g/bts traffic |
| title | OVERVIEW STUDY OF MOBILE NETWORK TRAFFIC FOR BTS STATIONS |
| title_full | OVERVIEW STUDY OF MOBILE NETWORK TRAFFIC FOR BTS STATIONS |
| title_fullStr | OVERVIEW STUDY OF MOBILE NETWORK TRAFFIC FOR BTS STATIONS |
| title_full_unstemmed | OVERVIEW STUDY OF MOBILE NETWORK TRAFFIC FOR BTS STATIONS |
| title_short | OVERVIEW STUDY OF MOBILE NETWORK TRAFFIC FOR BTS STATIONS |
| title_sort | overview study of mobile network traffic for bts stations |
| topic | 5g traffic base transceiver station 5g bts 5g traffic 5g/bts traffic |
| url | https://vujs.vn//api/view.aspx?cid=93f7b4a1-4e40-4ce9-a763-21b553ba752e |
| work_keys_str_mv | AT hoangvanthucphamvanngocdoanthithanhthaovuchienthangphamthanhnammacthiphuong overviewstudyofmobilenetworktrafficforbtsstations |