Clustering Strategies in Satellite-Aided Communications
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916953375899648 |
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| author | Thi-Thanh, Tam Ninh Quan, Nguyen Minh Tung, Do Son Van Chien, Trinh Tran, Hung |
| author_facet | Thi-Thanh, Tam Ninh Quan, Nguyen Minh Tung, Do Son Van Chien, Trinh Tran, Hung |
| contents | With the rapid advancement of next-generation satellite networks, addressing clustering tasks, user grouping, and efficient link management has become increasingly critical to optimize network performance and reduce interference. In this paper, we provide a comprehensive overview of modern clustering approaches based on machine learning and heuristic algorithms. The experimental results indicate that improved machine learning techniques and graph theory-based methods deliver significantly better performance and scalability than conventional clustering methods, such as the pure clustering algorithm examined in previous research. These advantages are especially evident in large-scale satellite network scenarios. Furthermore, the paper outlines potential research directions and discusses integrated, multi-dimensional solutions to enhance adaptability and efficiency in future satellite communication. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13701 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Clustering Strategies in Satellite-Aided Communications Thi-Thanh, Tam Ninh Quan, Nguyen Minh Tung, Do Son Van Chien, Trinh Tran, Hung Information Theory With the rapid advancement of next-generation satellite networks, addressing clustering tasks, user grouping, and efficient link management has become increasingly critical to optimize network performance and reduce interference. In this paper, we provide a comprehensive overview of modern clustering approaches based on machine learning and heuristic algorithms. The experimental results indicate that improved machine learning techniques and graph theory-based methods deliver significantly better performance and scalability than conventional clustering methods, such as the pure clustering algorithm examined in previous research. These advantages are especially evident in large-scale satellite network scenarios. Furthermore, the paper outlines potential research directions and discusses integrated, multi-dimensional solutions to enhance adaptability and efficiency in future satellite communication. |
| title | Clustering Strategies in Satellite-Aided Communications |
| topic | Information Theory |
| url | https://arxiv.org/abs/2509.13701 |