Prediction of LTE Cell Degradation Using Hidden Markov Model
No Thumbnail Available
Date
2023-08
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Addis Ababa University
Abstract
Long-Term Evolution (LTE) networks play a crucial role in providing high-speed wireless communication services. However, operators often have incomplete awareness of the overall state of their LTE networks due to the vast number of cells, the dynamic nature of LTE networks operations, complex interference scenarios, and huge number of key performance indicators (KPIs). This thesis presents a novel approach to predict LTE cell degradation levels using Hidden Markov Models (HMM). HMMs are a class of probabilistic models that can be used to capture the dynamic nature of LTE networks. HMMs model the sequential occurrence of cell degradation events, which provides network operators statistical insights into the future state of cells based on historical data.
To develop our prediction model, we used KPIs, such as average traffic volume, number of Reference Signal Received Power (RSRP) measurement report, and number of outgoing handover requests as observation datasets. These KPIs are clustered into six unique observation sequences, which form the basis for our model training. Then, the Baum-Welch algorithm is applied to train and obtain the HMM parameters for modeling the cell degradation. The results of the study convincingly demonstrate the performance scores of the HMM prediction model. With an average of 23 observation lengths, the HMM achieved an average accuracy of 93.12%, F1 score of 91.81% and a precision of 92.82%. These metrics illustrate the effectiveness of using the proposed HMM approach in predicting LTE cell degradation levels.
This research addresses the challenges of monitoring and analyzing LTE cell degradation events by proposing a comprehensive methodology for LTE cell degradation prediction using HMM and KPIs. The timely provision of predictions enables operators to proactively identify and address potential network issues, optimizing network performance and enhancing quality of service.
The main limitations of this study are that it was conducted on a small number of cells and only four degradation states. Future work should test the approach on a larger number of cells with various KPIs and complex states using different types of HMMs.