Bagging ensemble mean-shift Gaussian kernelized clustering based D2D connectivity enabled communication for 5G networks
The advent of the fifth-generation (5 G) network integrates a spectrum of powerful technologies. Among these, Device-to-Device (D2D) communication stands as a focal point in research due to its expansive array of applications. Within the realm of 5 G, D2D communication offers support for high data r...
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Elsevier
2024-03-01
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| Series: | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772671123002954 |
| _version_ | 1797256284517433344 |
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| author | Varadala Sridhar T. Annamani M. Renuka V. Vinay Kumar Arunkumar Madupu |
| author_facet | Varadala Sridhar T. Annamani M. Renuka V. Vinay Kumar Arunkumar Madupu |
| author_sort | Varadala Sridhar |
| collection | DOAJ |
| description | The advent of the fifth-generation (5 G) network integrates a spectrum of powerful technologies. Among these, Device-to-Device (D2D) communication stands as a focal point in research due to its expansive array of applications. Within the realm of 5 G, D2D communication offers support for high data rates and ultra-reliable, low-latency communications. While machine learning-based approaches have been devised to facilitate D2D communication in 5 G, ensuring robust connectivity and optimal network throughput remains a persisting challenge. Therefore, the bagging ensemble mean-shift Gaussian kernelized clustering-based D2D Connectivity (BEMSGKC-DC) algorithm is designed to enhance device-to-device communication in 5 G Cellular Networks with better packet delivery ratio and throughput. The proposed BEMSGKC-DC algorithm is proposed to enable massive device connectivity while improving the overall network performance. This can be achieved by employing bootstrap aggregating clustering where it clusters the devices via forming a set of base clusters i.e., Mean-Shift Gaussian kernelized clustering algorithm. To further enhance the performance of the algorithm, this paper also computes the resources of the devices in the clustering process using the Gaussian kernel function. All the weak cluster outcomes are merged to acquire the final clustering output with lower error. Through the resultant clustering of the device, the connectivity between D2D is ensured and thereby improves the data communication in the 5 G network. The result demonstrates that the BEMSGKC-DC algorithm optimizes the connectivity with lower energy, latency, and higher data delivery ratio and packet loss rate than the conventional algorithms. |
| first_indexed | 2024-03-08T18:11:35Z |
| format | Article |
| id | doaj.art-85c1742ee60f4ddca650aef1ea66b483 |
| institution | Directory Open Access Journal |
| issn | 2772-6711 |
| language | English |
| last_indexed | 2024-04-24T22:19:18Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| spelling | doaj.art-85c1742ee60f4ddca650aef1ea66b4832024-03-20T06:11:42ZengElseviere-Prime: Advances in Electrical Engineering, Electronics and Energy2772-67112024-03-017100400Bagging ensemble mean-shift Gaussian kernelized clustering based D2D connectivity enabled communication for 5G networksVaradala Sridhar0T. Annamani1M. Renuka2V. Vinay Kumar3Arunkumar Madupu4Department of Electronics and Communication Engineering, Vidya Jyothi institute of Technology, Aziz Nagar, C, B, Post, India; Corresponding author.Assistant Professor, Department of Electronics and Communication Engineering, Anurag University, Ghatkesar, Telangana, IndiaDepartment of Electronics and Communication Engineering, Vidya Jyothi institute of Technology, Aziz Nagar, C, B, Post, IndiaAssistant Professor, Department of Electronics and Communication Engineering, Anurag University, Ghatkesar, Telangana, IndiaAssociate Professor, Department of Electronics and Communication Engineering, Malla Reddy College of Engineering and Technology, Hyderabad, Telangana, IndiaThe advent of the fifth-generation (5 G) network integrates a spectrum of powerful technologies. Among these, Device-to-Device (D2D) communication stands as a focal point in research due to its expansive array of applications. Within the realm of 5 G, D2D communication offers support for high data rates and ultra-reliable, low-latency communications. While machine learning-based approaches have been devised to facilitate D2D communication in 5 G, ensuring robust connectivity and optimal network throughput remains a persisting challenge. Therefore, the bagging ensemble mean-shift Gaussian kernelized clustering-based D2D Connectivity (BEMSGKC-DC) algorithm is designed to enhance device-to-device communication in 5 G Cellular Networks with better packet delivery ratio and throughput. The proposed BEMSGKC-DC algorithm is proposed to enable massive device connectivity while improving the overall network performance. This can be achieved by employing bootstrap aggregating clustering where it clusters the devices via forming a set of base clusters i.e., Mean-Shift Gaussian kernelized clustering algorithm. To further enhance the performance of the algorithm, this paper also computes the resources of the devices in the clustering process using the Gaussian kernel function. All the weak cluster outcomes are merged to acquire the final clustering output with lower error. Through the resultant clustering of the device, the connectivity between D2D is ensured and thereby improves the data communication in the 5 G network. The result demonstrates that the BEMSGKC-DC algorithm optimizes the connectivity with lower energy, latency, and higher data delivery ratio and packet loss rate than the conventional algorithms.http://www.sciencedirect.com/science/article/pii/S27726711230029545 G cellular networksD2D connectivityMean-shift Gaussian kernelized clusteringbootstrap aggregating clustering |
| spellingShingle | Varadala Sridhar T. Annamani M. Renuka V. Vinay Kumar Arunkumar Madupu Bagging ensemble mean-shift Gaussian kernelized clustering based D2D connectivity enabled communication for 5G networks e-Prime: Advances in Electrical Engineering, Electronics and Energy 5 G cellular networks D2D connectivity Mean-shift Gaussian kernelized clustering bootstrap aggregating clustering |
| title | Bagging ensemble mean-shift Gaussian kernelized clustering based D2D connectivity enabled communication for 5G networks |
| title_full | Bagging ensemble mean-shift Gaussian kernelized clustering based D2D connectivity enabled communication for 5G networks |
| title_fullStr | Bagging ensemble mean-shift Gaussian kernelized clustering based D2D connectivity enabled communication for 5G networks |
| title_full_unstemmed | Bagging ensemble mean-shift Gaussian kernelized clustering based D2D connectivity enabled communication for 5G networks |
| title_short | Bagging ensemble mean-shift Gaussian kernelized clustering based D2D connectivity enabled communication for 5G networks |
| title_sort | bagging ensemble mean shift gaussian kernelized clustering based d2d connectivity enabled communication for 5g networks |
| topic | 5 G cellular networks D2D connectivity Mean-shift Gaussian kernelized clustering bootstrap aggregating clustering |
| url | http://www.sciencedirect.com/science/article/pii/S2772671123002954 |
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