A Machine Learning Approach to Enhance the Performance of D2D-Enabled Clustered Networks
Clustering has been suggested as an effective technique to enhance the performance of multicasting networks. Typically, a cluster head is selected to broadcast the cached content to its cluster members utilizing Device-to-Device (D2D) communication. However, some users can attain better performance...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9328769/ |
| _version_ | 1819175705617891328 |
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| author | Saad Aslam Fakhrul Alam Syed Faraz Hasan Mohammad A. Rashid |
| author_facet | Saad Aslam Fakhrul Alam Syed Faraz Hasan Mohammad A. Rashid |
| author_sort | Saad Aslam |
| collection | DOAJ |
| description | Clustering has been suggested as an effective technique to enhance the performance of multicasting networks. Typically, a cluster head is selected to broadcast the cached content to its cluster members utilizing Device-to-Device (D2D) communication. However, some users can attain better performance by being connected with the Evolved Node B (eNB) rather than being in the clusters. In this article, we apply machine learning algorithms, namely Support Vector Machine, Random Forest, and Deep Neural Network to identify the users that should be serviced by the eNB. We therefore propose a mixed-mode content distribution scheme where the cluster heads and eNB service the two segregated groups of users to improve the performance of existing clustering schemes. A D2D-enabled multicasting scenario has been set up to perform a comprehensive simulation study that demonstrates that by utilizing the mixed-mode scheme, the performance of individual users, as well as the whole network, improve significantly in terms of throughput, energy consumption, and fairness. This study also demonstrates the trade-off between eNB loading and performance improvement for various parameters. |
| first_indexed | 2024-12-22T20:59:07Z |
| format | Article |
| id | doaj.art-e6bbff1381574311b6ad5fa433575354 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T20:59:07Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e6bbff1381574311b6ad5fa4335753542022-12-21T18:12:52ZengIEEEIEEE Access2169-35362021-01-019161141613210.1109/ACCESS.2021.30530459328769A Machine Learning Approach to Enhance the Performance of D2D-Enabled Clustered NetworksSaad Aslam0https://orcid.org/0000-0003-3890-1245Fakhrul Alam1https://orcid.org/0000-0002-2455-3131Syed Faraz Hasan2Mohammad A. Rashid3https://orcid.org/0000-0002-0844-5819Department of Mechanical & Electrical Engineering, School of Food & Advanced Technology, Massey University, Auckland, New ZealandDepartment of Mechanical & Electrical Engineering, School of Food & Advanced Technology, Massey University, Auckland, New ZealandDepartment of Mechanical & Electrical Engineering, School of Food & Advanced Technology, Massey University, Auckland, New ZealandDepartment of Mechanical & Electrical Engineering, School of Food & Advanced Technology, Massey University, Auckland, New ZealandClustering has been suggested as an effective technique to enhance the performance of multicasting networks. Typically, a cluster head is selected to broadcast the cached content to its cluster members utilizing Device-to-Device (D2D) communication. However, some users can attain better performance by being connected with the Evolved Node B (eNB) rather than being in the clusters. In this article, we apply machine learning algorithms, namely Support Vector Machine, Random Forest, and Deep Neural Network to identify the users that should be serviced by the eNB. We therefore propose a mixed-mode content distribution scheme where the cluster heads and eNB service the two segregated groups of users to improve the performance of existing clustering schemes. A D2D-enabled multicasting scenario has been set up to perform a comprehensive simulation study that demonstrates that by utilizing the mixed-mode scheme, the performance of individual users, as well as the whole network, improve significantly in terms of throughput, energy consumption, and fairness. This study also demonstrates the trade-off between eNB loading and performance improvement for various parameters.https://ieeexplore.ieee.org/document/9328769/Clustering algorithmcontent multicastingD2D enabled networksdeep neural networkseNB loadingmachine learning |
| spellingShingle | Saad Aslam Fakhrul Alam Syed Faraz Hasan Mohammad A. Rashid A Machine Learning Approach to Enhance the Performance of D2D-Enabled Clustered Networks IEEE Access Clustering algorithm content multicasting D2D enabled networks deep neural networks eNB loading machine learning |
| title | A Machine Learning Approach to Enhance the Performance of D2D-Enabled Clustered Networks |
| title_full | A Machine Learning Approach to Enhance the Performance of D2D-Enabled Clustered Networks |
| title_fullStr | A Machine Learning Approach to Enhance the Performance of D2D-Enabled Clustered Networks |
| title_full_unstemmed | A Machine Learning Approach to Enhance the Performance of D2D-Enabled Clustered Networks |
| title_short | A Machine Learning Approach to Enhance the Performance of D2D-Enabled Clustered Networks |
| title_sort | machine learning approach to enhance the performance of d2d enabled clustered networks |
| topic | Clustering algorithm content multicasting D2D enabled networks deep neural networks eNB loading machine learning |
| url | https://ieeexplore.ieee.org/document/9328769/ |
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