A deep dive into node-level analysis with fusion RNN model for smart LTE network monitoring
This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2023.
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| Định dạng: | Luận văn |
| Ngôn ngữ: | English |
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Brac University
2024
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| Truy cập trực tuyến: | http://hdl.handle.net/10361/23110 |
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10361-231102024-06-04T21:03:19Z A deep dive into node-level analysis with fusion RNN model for smart LTE network monitoring Islam, Md Rashidul Alam, Golam Rabiul Department of Computer Science and Engineering, Brac University LTE networks Machine learning Deep learning Mobile network capacity Resource management Long-Term Evolution (Telecommunications) Machine learning Deep learning Resource management This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 42-45). Predicting and understanding traffic patterns have become important objectives for maintaining the Quality of Service (QoS) standard in network management. This change stems from analyzing the data usage on cellular internet networks. Cellular network optimiser frequently employ a variety of data traffic prediction algorithms for this reason. Traditional traffic projections are often made at the high-level or generously large regional cluster level and therefore has the lacking in precised forecation. Furthermore, it is difficult to obtain information on eNodeB-level utilisation with regard to traffic predictions. As a result, using the conventional approach causes user experience degradation or unnecessary network expansion. Developing a traffic forecasting model with the aid of multivariate feature inputs and deep learning techniques was one of the objective of this research. It deals with extensive 6.2 million real network time series LTE data traffic and other associated characteristics, including eNodeB-wise PRB utilisation. A cutting-edge fusion model based on Deep Learning algorithms is suggested. Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU) are three deep learning algorithms that when combined allow for eNodeB-level traffic forecasting and eNodeB-wise anticipated PRB utilisation.The proposed fusion model’s R2 score is 0.8034, outperforms the conventional state-if-the-art models. This study also proposed a unique method that thoroughly examines individual nodes for the Smart Network Monitor. This approach follows adjustments made to soft capacity parameters at the eNodeB level, aiming for immediate improvement or long-term network growth to meet a consistent QoS standard. The algorithm relies on expected PRB utilization. Md Rashidul Islam M.Sc. in Computer Science 2024-06-04T05:37:07Z 2024-06-04T05:37:07Z ©2023 2023-09 Thesis ID 20366008 http://hdl.handle.net/10361/23110 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 55 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
LTE networks Machine learning Deep learning Mobile network capacity Resource management Long-Term Evolution (Telecommunications) Machine learning Deep learning Resource management |
| spellingShingle |
LTE networks Machine learning Deep learning Mobile network capacity Resource management Long-Term Evolution (Telecommunications) Machine learning Deep learning Resource management Islam, Md Rashidul A deep dive into node-level analysis with fusion RNN model for smart LTE network monitoring |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, 2023. |
| author2 |
Alam, Golam Rabiul |
| author_facet |
Alam, Golam Rabiul Islam, Md Rashidul |
| format |
Thesis |
| author |
Islam, Md Rashidul |
| author_sort |
Islam, Md Rashidul |
| title |
A deep dive into node-level analysis with fusion RNN model for smart LTE network monitoring |
| title_short |
A deep dive into node-level analysis with fusion RNN model for smart LTE network monitoring |
| title_full |
A deep dive into node-level analysis with fusion RNN model for smart LTE network monitoring |
| title_fullStr |
A deep dive into node-level analysis with fusion RNN model for smart LTE network monitoring |
| title_full_unstemmed |
A deep dive into node-level analysis with fusion RNN model for smart LTE network monitoring |
| title_sort |
deep dive into node-level analysis with fusion rnn model for smart lte network monitoring |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/23110 |
| work_keys_str_mv |
AT islammdrashidul adeepdiveintonodelevelanalysiswithfusionrnnmodelforsmartltenetworkmonitoring AT islammdrashidul deepdiveintonodelevelanalysiswithfusionrnnmodelforsmartltenetworkmonitoring |
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