Congestion Control Prediction Model for 5G Environment Based on Supervised and Unsupervised Machine Learning Approach
With the emergence of 5G technology, congestion control has become a vital challenge to be addressed in order to have efficient communication. There are several congestion control models that have been proposed to control and predict the possible congestion in 5G technology. However, finding the opt...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10562283/ |
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| author | Mohammed B. Alshawki Ihab Ahmed Najm Alaa Khalaf Hamoud |
| author_facet | Mohammed B. Alshawki Ihab Ahmed Najm Alaa Khalaf Hamoud |
| author_sort | Mohammed B. Alshawki |
| collection | DOAJ |
| description | With the emergence of 5G technology, congestion control has become a vital challenge to be addressed in order to have efficient communication. There are several congestion control models that have been proposed to control and predict the possible congestion in 5G technology. However, finding the optimal congestion control model is an important yet challenging task. In this paper, we examine the supervised and unsupervised machine learning approaches to the task of predicting the possible node that causes congestion in the 5G environment. Due to the huge variance in the domains of the data set columns, measuring the prediction’s consistency was not an easy task. During our study, we tested twenty-six supervised and seven clustering algorithms. Finally, and based on the performance criteria, we have identified the best five algorithms out of the studied algorithms. |
| format | Article |
| id | doaj-art-e1f2c11afc3449d3849afd7fbbfbc0f1 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e1f2c11afc3449d3849afd7fbbfbc0f12025-08-20T02:45:42ZengIEEEIEEE Access2169-35362024-01-0112911279113910.1109/ACCESS.2024.341686310562283Congestion Control Prediction Model for 5G Environment Based on Supervised and Unsupervised Machine Learning ApproachMohammed B. Alshawki0https://orcid.org/0000-0003-1619-2927Ihab Ahmed Najm1https://orcid.org/0000-0002-1834-5408Alaa Khalaf Hamoud2https://orcid.org/0000-0001-7612-1835Department of Computer Science, University of Kufa, Najaf, IraqDepartment of Mathematics, University of Tikrit, Tikrit, IraqDepartment of Cybersecurity, University of Basrah, Basrah, IraqWith the emergence of 5G technology, congestion control has become a vital challenge to be addressed in order to have efficient communication. There are several congestion control models that have been proposed to control and predict the possible congestion in 5G technology. However, finding the optimal congestion control model is an important yet challenging task. In this paper, we examine the supervised and unsupervised machine learning approaches to the task of predicting the possible node that causes congestion in the 5G environment. Due to the huge variance in the domains of the data set columns, measuring the prediction’s consistency was not an easy task. During our study, we tested twenty-six supervised and seven clustering algorithms. Finally, and based on the performance criteria, we have identified the best five algorithms out of the studied algorithms.https://ieeexplore.ieee.org/document/10562283/Machine learningcongestion control5Gsupervised MLunsupervised ML |
| spellingShingle | Mohammed B. Alshawki Ihab Ahmed Najm Alaa Khalaf Hamoud Congestion Control Prediction Model for 5G Environment Based on Supervised and Unsupervised Machine Learning Approach IEEE Access Machine learning congestion control 5G supervised ML unsupervised ML |
| title | Congestion Control Prediction Model for 5G Environment Based on Supervised and Unsupervised Machine Learning Approach |
| title_full | Congestion Control Prediction Model for 5G Environment Based on Supervised and Unsupervised Machine Learning Approach |
| title_fullStr | Congestion Control Prediction Model for 5G Environment Based on Supervised and Unsupervised Machine Learning Approach |
| title_full_unstemmed | Congestion Control Prediction Model for 5G Environment Based on Supervised and Unsupervised Machine Learning Approach |
| title_short | Congestion Control Prediction Model for 5G Environment Based on Supervised and Unsupervised Machine Learning Approach |
| title_sort | congestion control prediction model for 5g environment based on supervised and unsupervised machine learning approach |
| topic | Machine learning congestion control 5G supervised ML unsupervised ML |
| url | https://ieeexplore.ieee.org/document/10562283/ |
| work_keys_str_mv | AT mohammedbalshawki congestioncontrolpredictionmodelfor5genvironmentbasedonsupervisedandunsupervisedmachinelearningapproach AT ihabahmednajm congestioncontrolpredictionmodelfor5genvironmentbasedonsupervisedandunsupervisedmachinelearningapproach AT alaakhalafhamoud congestioncontrolpredictionmodelfor5genvironmentbasedonsupervisedandunsupervisedmachinelearningapproach |