Machine Learning for Improved Root Cause Analysis of LTE Network Accessibility and Integrity Degradation
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Date
2023-09
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Addis Ababa University
Abstract
Long Term Evolution (LTE) networks are essential for enabling high-speed, reliable
communication and data transmission. However, the accessibility and integrity of LTE
networks can degrade due to a variety of factors, such as congestion, coverage, and configuration
problems. Root cause analysis (RCA) is a process for identifying the underlying
causes of degradation. However, RCA can be time-consuming and labor-intensive. Machine
learning can be used to enhance RCA by identifying patterns and trends in data
that can be used to identify the root causes of problems. Limited work exists on machine
learning-enabled RCA for LTE networks.
This thesis proposes a machine learning-enabled approach, specifically Convolutional
Neural Network (CNN) and SHapley Additive exPlanations (SHAP), for RCA of LTE
network performance degradation. The approach was evaluated using key performance
indicators (KPIs) and counters data collected from LTE network of ethio telecom, a major
operator in Ethiopia.
The main causes of reduced network accessibility are failure caused by the Mobility
Management Entity (MME), the average number of users, and handover failures. Similarly,
the underlying causes of degraded accessibility at the cell level are failure caused
by MME, control channel element (CCE) utilization, and paging utilization. For network
integrity, which is measured by user throughput, the main causes of degradation are the
high number of active users, high downlink Physical Resource Block (PRB) utilization,
poor Channel Quality Indicator (CQI), and coverage issues. At the cell level, the main
factors are downlink PRB utilization, unfavorable CQI values, and high downlink block
error rate.
For the given data, the model’s sensitivity for network accessibility and integrity at the
cell level is 82.8% and 95.5%, respectively. These results demonstrate the potential of the
proposed approach to accurately identify degradation instances. The proposed approach
using deep learning and SHAP offers reusability, high-dimensionality support, geographic
scalability, and time resolution for improved performance analysis in networks of all sizes.
Network operators can improve network performance by identifying and addressing the
root causes of degradation.
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Keywords
Accessibility, CNN, Integrity, LTE, Root Cause Analysis, SHAP