Capacity Enhanced-Energy Efficient Base Station Deployment Using Genetic Algorithm
| dc.contributor.advisor | Yihenew, Wondie (PhD) | |
| dc.contributor.author | Tabor, Birru | |
| dc.date.accessioned | 2020-03-11T05:50:46Z | |
| dc.date.accessioned | 2023-11-04T15:13:14Z | |
| dc.date.available | 2020-03-11T05:50:46Z | |
| dc.date.available | 2023-11-04T15:13:14Z | |
| dc.date.issued | 2020-02 | |
| dc.description.abstract | Unparalleled increasing demands for high capacity and consistent service quality on cellular network have been challenging for telecom operators. However, current deployed and existing radio access network is significantly behind the growth. This promotes operators to network upgrade, expansion and base station (BS) densification. Operators are also trying to mix macro and small cell on their radio access network as a potential solution to meet their customer demands by enhancing their network capacity and coverage extension. However, addition of the small cell on existing network increases energy consumption. This thesis study considers energy efficient BS deployment for enhancement of LTE network capacity. For this purpose, small cell deployment underlay to the existing macro BS is used in outdoor scenario in 2x2 square kilometer area located in Addis Ababa. Candidate locations for the small cell first selected mainly based on traffic distributions in the selected area of study. Then radio propagation simulations performed using WinProp radio planning and simulation tool followed by Genetic algorithm based optimization to find out the optimal number and locations of the small cell obtaining enhanced capacity and improved energy efficiency with minimized additional power consumption. The result analysis is observed in MatLab implementation. Finally, aggregate capacity and energy efficiency have been evaluated. The result shows that both the aggregate network capacity and energy efficiency increased with number of small cell. There is 77.94% capacity and 24.7% energy efficiency improvement as compared to the original macro BS only network, which respectively requires 82 and 59 small cells transmitting at 0.5-watt power. At the same time, improvement of capacity requires 18 small cell while it takes 23 and above small cell for energy efficiency to be improved. For small cell more than 59, the energy efficiency start declining which indicates small cell deployment beyond this value has no importance as it declines energy efficiency. The aggregate network capacity has improved by 66.99% when selection of the small cell limited on its impact on energy efficiency. The maximum energy efficiency achieved is 24.7%, 22.32%, 17.88% and 11.12% respectively for small cell transmitting at 0.5watt, 2watt, 5watt and 10watt. This result can possibly be improved further by using different techniques such as sleep mode and cell zooming operations. | en_US |
| dc.identifier.uri | etd.aau.edu.et/handle/123456789/21107 | |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/21107 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Energy efficiency | en_US |
| dc.subject | Base station deployment | en_US |
| dc.subject | small cell | en_US |
| dc.subject | Capacity enhancement | en_US |
| dc.subject | LTE | en_US |
| dc.title | Capacity Enhanced-Energy Efficient Base Station Deployment Using Genetic Algorithm | en_US |
| dc.type | Thesis | en_US |