Intelligent relay selection in 5G D2D communication: leveraging machine learning for enhanced coverage
In the evolving landscape of 5G networks, Device-to-Device (D2D) communication has emerged as a significant technology to offload traffic, enhance user experience, and expand network coverage. While D2D promises seamless connectivity, efficient relay selection remains a challenge, particularly in dy...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2024
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| Online Access: | http://journalarticle.ukm.my/25731/ http://journalarticle.ukm.my/25731/1/11.pdf |
| _version_ | 1848816435467386880 |
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| author | Shamganth Kumarapandian, Gopi Krishna Pasam, |
| author_facet | Shamganth Kumarapandian, Gopi Krishna Pasam, |
| author_sort | Shamganth Kumarapandian, |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | In the evolving landscape of 5G networks, Device-to-Device (D2D) communication has emerged as a significant technology to offload traffic, enhance user experience, and expand network coverage. While D2D promises seamless connectivity, efficient relay selection remains a challenge, particularly in dynamic communication environments. This paper introduces an intelligent relay selection mechanism that leverages machine learning, specifically Artificial Neural Networks (ANN) with Radial Basis Function Neural Network (RBFNN) approach, to optimize D2D communication in 5G networks. By integrating a threshold-based relay selection and combining with the predictive capabilities of ANN, we aim to improve overall network coverage. Our method dynamically adjusts selection criteria based on real-time network conditions, ensuring optimal relay selection and minimizing communication breakdowns. Initial simulation results reveal that our approach exceeds traditional techniques, showcasing significant improvements in the coverage area, data output, and reduced inactivity. This research shows the way for a more adaptive, intelligent and efficient D2D communication framework in 5G systems. |
| first_indexed | 2025-11-15T01:05:50Z |
| format | Article |
| id | oai:generic.eprints.org:25731 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T01:05:50Z |
| publishDate | 2024 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:257312025-08-12T01:43:16Z http://journalarticle.ukm.my/25731/ Intelligent relay selection in 5G D2D communication: leveraging machine learning for enhanced coverage Shamganth Kumarapandian, Gopi Krishna Pasam, In the evolving landscape of 5G networks, Device-to-Device (D2D) communication has emerged as a significant technology to offload traffic, enhance user experience, and expand network coverage. While D2D promises seamless connectivity, efficient relay selection remains a challenge, particularly in dynamic communication environments. This paper introduces an intelligent relay selection mechanism that leverages machine learning, specifically Artificial Neural Networks (ANN) with Radial Basis Function Neural Network (RBFNN) approach, to optimize D2D communication in 5G networks. By integrating a threshold-based relay selection and combining with the predictive capabilities of ANN, we aim to improve overall network coverage. Our method dynamically adjusts selection criteria based on real-time network conditions, ensuring optimal relay selection and minimizing communication breakdowns. Initial simulation results reveal that our approach exceeds traditional techniques, showcasing significant improvements in the coverage area, data output, and reduced inactivity. This research shows the way for a more adaptive, intelligent and efficient D2D communication framework in 5G systems. Penerbit Universiti Kebangsaan Malaysia 2024-09 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/25731/1/11.pdf Shamganth Kumarapandian, and Gopi Krishna Pasam, (2024) Intelligent relay selection in 5G D2D communication: leveraging machine learning for enhanced coverage. Jurnal Kejuruteraan, 36 (5). pp. 1909-1919. ISSN 0128-0198 https://www.ukm.my/jkukm/volume-3605-2024/ |
| spellingShingle | Shamganth Kumarapandian, Gopi Krishna Pasam, Intelligent relay selection in 5G D2D communication: leveraging machine learning for enhanced coverage |
| title | Intelligent relay selection in 5G D2D communication: leveraging machine learning for enhanced coverage |
| title_full | Intelligent relay selection in 5G D2D communication: leveraging machine learning for enhanced coverage |
| title_fullStr | Intelligent relay selection in 5G D2D communication: leveraging machine learning for enhanced coverage |
| title_full_unstemmed | Intelligent relay selection in 5G D2D communication: leveraging machine learning for enhanced coverage |
| title_short | Intelligent relay selection in 5G D2D communication: leveraging machine learning for enhanced coverage |
| title_sort | intelligent relay selection in 5g d2d communication: leveraging machine learning for enhanced coverage |
| url | http://journalarticle.ukm.my/25731/ http://journalarticle.ukm.my/25731/ http://journalarticle.ukm.my/25731/1/11.pdf |