Neural Network based 3G Mobile Sites Fault Prediction: A Case Study in Addis Ababa, Ethiopia
| dc.contributor.advisor | Yihenew, Wondie (PhD) | |
| dc.contributor.author | Asmelash, Tesfay | |
| dc.date.accessioned | 2018-12-21T08:39:47Z | |
| dc.date.accessioned | 2023-11-04T15:13:09Z | |
| dc.date.available | 2018-12-21T08:39:47Z | |
| dc.date.available | 2023-11-04T15:13:09Z | |
| dc.date.issued | 2018-11 | |
| dc.description.abstract | As cellular mobile networks are evolving in technology and service type, the number of mobile network site infrastructure is increasing. Nowadays, faulty cellular mobile network sites per day in ethio telecom are significant in number and have big impact to customers and operator in QoS, revenue and maintenance cost. Fault maintenance techniques commonly applied by ethio telecom is corrective maintenance approach. This only helps to recover services after interruption. However, it is important to implement the proactive maintenance approach to make mobile sites reliable and available. This helps to provide services according to standards and improve the quality of service delivery to customers. To mitigate mobile network site faults before happening, fault occurrence time prediction is an important technique for the implementation of proactive maintenance strategy. This Neural Network based 3G Mobile Fault Occurrence Prediction research work is conducted based on the Nonlinear Auto regressive (NAR) Neural Network time series prediction method using Addis Ababa 3G mobile sites in a case study. To train the neural network 15,950 actual fault occurrence time data are used. The algorithm used to train the neural network is Levenberg-Marquardt which is fast and efficient, and an iterative approach of hidden layer neuron number selection is applied. Finally, the best model is selected with minimum value of mean square error of prediction. Also, the model is tested with actual fault occurrence time which was not used in the training and achieved 90.71% in prediction. Therefore, it is efficient in prediction accuracy, fast and adaptive with future data. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/15227 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | AAU | en_US |
| dc.subject | Mobile Site | en_US |
| dc.subject | Fault | en_US |
| dc.subject | Corrective Maintenance | en_US |
| dc.subject | Proactive Maintenance | en_US |
| dc.subject | Fault Occurrence Time | en_US |
| dc.subject | Prediction | en_US |
| dc.subject | Neural Network | en_US |
| dc.subject | Time Series | en_US |
| dc.title | Neural Network based 3G Mobile Sites Fault Prediction: A Case Study in Addis Ababa, Ethiopia | en_US |
| dc.type | Thesis | en_US |