Root Cause Analysis of Insufficient Effective Throughput in 4G Core Network using Long Short-Term Memory Networks
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Date
2025-06
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Addis Ababa University
Abstract
In the world of telecommunications today, the 4G Core Network (4G CN) is a critical
enabler of higher speed data connectivity for mobile internet users. Unfortunately, many
telecom operators continue to struggle with the persistent issue of Effective Throughput
(ET), which decreases service quality and user experience. Inspired by these issues, this
thesis investigates the causes of ET degradation in various flows within the 4G CN while
emphasizing a shift toward higher degree of data-driven additional insight and the need to
implement proactive network management strategies.
To tackle the issue, a thesis on a Long Short-term Memory (LSTM) networks method,
for time series classification of performance degradation patterns in 4G CN have been
proposed. To improve model interpretability and transparency, apply SHapley Additive
exPlanations (SHAP) to provide insights into the contribution of feature behaviour on our
decisions, to the level of individual features. The methodology includes collecting real-world
network performance data, training the LSTM model, and applying SHAP to characterize
how each performance metric influenced behaviors contributing to throughput changes.
The resulting LSTM model produces good predictive accuracy with a performance of
92.10% and a ROC AUC score of 97.90%, confirming its effectiveness for the task of identifying
anomalies in ET. The SHAP analysis highlights that Client-Side Long RTT, Uplink
TCP Out-of-Order Rate, and Network Packet behaviours were important contributors to
IET behaviour. As a result, telecom operators can be empowered to dedicate increased
effort on high priority performance indicators for targeted optimization.
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Keywords
4G, CN, ET, LSTM, RCA, SHAP