Deep Learning-based Cell Performance Degradation Prediction
| dc.contributor.advisor | Dereje Hailemariam (PhD) | |
| dc.contributor.author | Betelehem Dagnaw | |
| dc.date.accessioned | 2024-09-03T08:24:47Z | |
| dc.date.available | 2024-09-03T08:24:47Z | |
| dc.date.issued | 2022-07 | |
| dc.description.abstract | In light of rapid developments in the telecommunications sector, there is a growing volume of generated data as well as high customer expectations regarding both cost and Quality of Service (QoS). For Mobile Network Operators (MNOs), the changing dynamics of radio networks pose challenges in coping with the increased number of network faults and outages, which both lead to performance degradation and increased operational expenditures (OPEX). Human expertise is required to diagnose, identify and x faults and outages. However, the increasing density of mobile cells and diversi cation of cell types are making this approach less feasible, both nancially and technically. In this paper, relying on the power of deep learning and the availability of large radio network data at MNOs, we propose a system that predicts the performance degradation of cells using key performance indicators (KPIs). Data collected from the Universal Mobile Telecommunications Service (UMTS) network of an operator located in Addis Ababa, Ethiopia, is used to build models in the system. The proposed system consists of a multivariate time series forecasting model, which forecasts KPIs in advance. In addition, a cell performance degradation detection model, which detects anomalous records in the KPI data based on the forecasting model outputs. Convolutional Long Short-Term Memory (ConvLSTM) and LSTM Autoencoders are cascaded for prediction and degradation detection. The results show that the system is capable of predicting KPIs with a Root Mean Square Error (RMSE) of 0.896 and a Mean Absolute Error (MAE) of 0.771, and detecting degradation with 98% accuracy. This research can therefore contribute signi cantly to improving network failure management systems by predicting the impact of upcoming cell performance degradations on network service before they occur. This research can therefore contribute signi cantly to improving network failure management systems by predicting the impact of upcoming cell performance degradations on network service before they occur. | |
| dc.identifier.uri | https://etd.aau.edu.et/handle/123456789/3462 | |
| dc.language.iso | en_US | |
| dc.publisher | Addis Ababa University | |
| dc.subject | Performance degradation detection | |
| dc.subject | Multivariate forecasting | |
| dc.subject | LSTM | |
| dc.subject | CNNLSTM | |
| dc.subject | ConvLSTM | |
| dc.subject | Deep Learning Network | |
| dc.subject | UMTS | |
| dc.subject | KPI | |
| dc.title | Deep Learning-based Cell Performance Degradation Prediction | |
| dc.type | Thesis |