Energy-efficient User Clustering for UAV-enabled Wireless Networks Using EM Algorithm
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2022
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| _version_ | 1866909834289348608 |
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| author | Janji, Salim Kliks, Adrian |
| author_facet | Janji, Salim Kliks, Adrian |
| contents | Unmanned Aerial Vehicles (UAVs) can be used to provide wireless connectivity to support the existing infrastructure in hot-spots or replace it in cases of destruction. UAV-enabled wireless provides several advantages in network performance due to drone small cells (DSCs) mobility despite the limited onboard energy. However, the problem of resource allocation has added complexity. In this paper, we propose an energy-efficient user clustering mechanism based on Gaussian mixture models (GMM) using a modified Expected-Maximization (EM) algorithm. The algorithm is intended to provide the initial user clustering and drone deployment upon which additional mechanisms can be employed to further enhance the system performance. The proposed algorithm improves the energy efficiency of the system by 25% and link reliability by 18.3% compared to other baseline methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2207_00873 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Energy-efficient User Clustering for UAV-enabled Wireless Networks Using EM Algorithm Janji, Salim Kliks, Adrian Networking and Internet Architecture Signal Processing Unmanned Aerial Vehicles (UAVs) can be used to provide wireless connectivity to support the existing infrastructure in hot-spots or replace it in cases of destruction. UAV-enabled wireless provides several advantages in network performance due to drone small cells (DSCs) mobility despite the limited onboard energy. However, the problem of resource allocation has added complexity. In this paper, we propose an energy-efficient user clustering mechanism based on Gaussian mixture models (GMM) using a modified Expected-Maximization (EM) algorithm. The algorithm is intended to provide the initial user clustering and drone deployment upon which additional mechanisms can be employed to further enhance the system performance. The proposed algorithm improves the energy efficiency of the system by 25% and link reliability by 18.3% compared to other baseline methods. |
| title | Energy-efficient User Clustering for UAV-enabled Wireless Networks Using EM Algorithm |
| topic | Networking and Internet Architecture Signal Processing |
| url | https://arxiv.org/abs/2207.00873 |