HYBRID OPTIMIZATION TECHNIQUES FOR MOBILITY-AWARE, ENERGY-EFFICIENT SMALL CELL DEPLOYMENT IN 5G NETWORKS
Fuente:
Zenodo
Salvato in:
| Autore principale: | |
|---|---|
| Natura: | Recurso digital |
| Lingua: | inglese |
| Pubblicazione: |
Zenodo
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866902039886299136 |
|---|---|
| author | Journal of Theoretical and Applied Information Technology |
| author_facet | Journal of Theoretical and Applied Information Technology |
| contents | <p><span>Expanding wireless communication networks is necessary to meet the growing number of mobile devices and the demand for faster internet. One practical way to increase network capacity and coverage in heavily populated regions is to deploy tiny cells. Smaller cells require more energy, increasing operating costs and negatively affecting the environment. Traditional deployment approaches ignore user mobility, despite its substantial impact on network performance. We present a strategy for microcell deployment in 5G networks, utilizing hybrid optimization techniques to address issues related to mobility awareness and energy efficiency. The planned teenTo improve data transfer capacity and increase user density in tiny cells, the suggested strategy clusters users using a Modified Smell-Bees Optimization (MSBO) algorithm. This research introduces a Gannet Optimal Induced Cuckoo Search (GOCS) approach to grouping microcells into optimal locations while accounting for various design limitations. This book lays out an Improved Coral Reef Optimization (ICRO) approach that takes reliability criteria into account for better coral reef optimization. Measures such as connection quality, user mobility, congestion rate, and mean time to failure are part of these criteria. Assisting in the setup of compact base stations is the goal of this plan. Simulations conducted in the Google Colab environment greatly enhance important Quality of Service (QoS) measures. The MSBO-GOCS-ICRO model is better than the well-known GSCP, TIPA, and ECM-BPSD models in many ways. For example, it cuts convergence time by 49%, increases the number of small base stations in use by 64%, and makes the network 154% more energy efficient. These findings indicate that the suggested approach is the optimal choice for the deployment of tiny cells in 5G networks.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18105156 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | HYBRID OPTIMIZATION TECHNIQUES FOR MOBILITY-AWARE, ENERGY-EFFICIENT SMALL CELL DEPLOYMENT IN 5G NETWORKS Journal of Theoretical and Applied Information Technology 5G networks, Small cell deployment, Hybrid optimization, Energy efficiency, Mobility management, Quality of Service. <p><span>Expanding wireless communication networks is necessary to meet the growing number of mobile devices and the demand for faster internet. One practical way to increase network capacity and coverage in heavily populated regions is to deploy tiny cells. Smaller cells require more energy, increasing operating costs and negatively affecting the environment. Traditional deployment approaches ignore user mobility, despite its substantial impact on network performance. We present a strategy for microcell deployment in 5G networks, utilizing hybrid optimization techniques to address issues related to mobility awareness and energy efficiency. The planned teenTo improve data transfer capacity and increase user density in tiny cells, the suggested strategy clusters users using a Modified Smell-Bees Optimization (MSBO) algorithm. This research introduces a Gannet Optimal Induced Cuckoo Search (GOCS) approach to grouping microcells into optimal locations while accounting for various design limitations. This book lays out an Improved Coral Reef Optimization (ICRO) approach that takes reliability criteria into account for better coral reef optimization. Measures such as connection quality, user mobility, congestion rate, and mean time to failure are part of these criteria. Assisting in the setup of compact base stations is the goal of this plan. Simulations conducted in the Google Colab environment greatly enhance important Quality of Service (QoS) measures. The MSBO-GOCS-ICRO model is better than the well-known GSCP, TIPA, and ECM-BPSD models in many ways. For example, it cuts convergence time by 49%, increases the number of small base stations in use by 64%, and makes the network 154% more energy efficient. These findings indicate that the suggested approach is the optimal choice for the deployment of tiny cells in 5G networks.</span></p> |
| title | HYBRID OPTIMIZATION TECHNIQUES FOR MOBILITY-AWARE, ENERGY-EFFICIENT SMALL CELL DEPLOYMENT IN 5G NETWORKS |
| topic | 5G networks, Small cell deployment, Hybrid optimization, Energy efficiency, Mobility management, Quality of Service. |
| url | https://doi.org/10.5281/zenodo.18105156 |