Hybrid Approach to Detect Fault Caused KPIs Anomaly in UMTS Cells
| dc.contributor.advisor | Surafel, Lemma (PhD) | |
| dc.contributor.author | Yared, Hawulte | |
| dc.date.accessioned | 2020-03-11T10:47:41Z | |
| dc.date.accessioned | 2023-11-04T15:13:16Z | |
| dc.date.available | 2020-03-11T10:47:41Z | |
| dc.date.available | 2023-11-04T15:13:16Z | |
| dc.date.issued | 2020-02-24 | |
| dc.description.abstract | Cellular networks usually suffer from failures or performance degradations due to several reasons, such as external interference, hardware/software bugs on network elements, power outages, or cable disconnections. Therefore, to avoid customer dissatisfaction and loss of revenue due to failures telecom operators need to detect and respond to performance anomalies of cellular networks instantly. However, the state of art performance anomaly detection framework (CELLPAD), which uses a correlation between two Key Performance Indicators (KPI)s as a means to detect anomaly is not capable of detecting anomalies happing during the off-pick hour, and; could not differentiate the causes of the anomalies. In this thesis, we propose a system model, which is capable of detecting anomalies happing at any traffic load and could differentiate the two causes of a correlational change anomaly. The proposed system model uses a newly added parameter called mean Received Total Wideband Power (RTWP) and filtering rules. To assess the performance of the proposed system model, we conducted an experiment using fourmonth performance counter data collected from 20 selected sites. The result shows that the proposed approach improves the detection of sudden drop anomaly by 10 % when compared to the state of the art statistical model, Weighted Moving Average (WMA). Besides, we can differentiate the two causes of correlational change anomaly with an F1 -score above 75 %. | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/123456789/21115 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Addis Ababa University | en_US |
| dc.subject | Anomaly detection | en_US |
| dc.subject | correlational change anomaly | en_US |
| dc.subject | KPI | en_US |
| dc.subject | sudden drop anomaly | en_US |
| dc.subject | RTWP | en_US |
| dc.subject | Universal Mobile Telecommunications System (UMTS) | en_US |
| dc.title | Hybrid Approach to Detect Fault Caused KPIs Anomaly in UMTS Cells | en_US |
| dc.type | Thesis | en_US |