Spatial and Temporal Traffic Distribution Modeling Using Statistical Techniques: The Case of UMTS Network in Addis Ababa, Ethiopia
| dc.contributor.advisor | Dereje, Hailemariam (PhD) | |
| dc.contributor.author | Woinshet, Taddele | |
| dc.date.accessioned | 2020-03-11T10:18:26Z | |
| dc.date.accessioned | 2023-11-04T15:13:16Z | |
| dc.date.available | 2020-03-11T10:18:26Z | |
| dc.date.available | 2023-11-04T15:13:16Z | |
| dc.date.issued | 2019-12 | |
| dc.description.abstract | In cellular mobile networks due to the emerge of different application services, the data traffic generated by these application services is increasing. Moreover, the traffic consumption pattern is changing in time and space. Understanding and characterizing the traffic demands and dynamics associated with different mobile services (e.g., knowing when and where users use dominant application services) is instrumental for operators. By doing so, operators can improve application services usability, optimizing network service quality and use it as an input for technical and business strategies. In the case of ethio telecom, the sole telecom service provider in Ethiopia, knowledge of the application services distribution is not a known practice. Therefore, the main purpose of this thesis work is modeling the spatial and temporal traffic distribution of observed application services for Addis Ababa Universal Mobile Telecommunication System (UMTS) network. In this regard, to study a temporal and spatial distribution analysis of the traffic density (the traffic load per unit area) for selected mobile application services, data traffic is collected from 739 bases Stations (BSs). To model the temporal distribution of the selected application services, which are Streaming, Social Networks and Web browsing, four candidate models: Normal, Lognormal, Weibull and Stable are used. Based on maximum log-likelihood and probability plot evaluation criteria, out of four models, a Stable distribution modeling best fit for this particular application services. Similarly, to model spatial distribution, out of the candidate four models, Weibull distribution model best fits for these particular services. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/21114 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | UMTS | en_US |
| dc.subject | Modeling | en_US |
| dc.subject | Application Services | en_US |
| dc.subject | Temporal | en_US |
| dc.subject | Spatial | en_US |
| dc.subject | Statistical Modelling | en_US |
| dc.subject | Weibull | en_US |
| dc.subject | Normal | en_US |
| dc.subject | Lognormal | en_US |
| dc.subject | Stable distribution | en_US |
| dc.title | Spatial and Temporal Traffic Distribution Modeling Using Statistical Techniques: The Case of UMTS Network in Addis Ababa, Ethiopia | en_US |
| dc.type | Thesis | en_US |