Anomaly Detection of LTE Cells using KNN Algorithms: The Case of Addis Ababa
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Date
2019-12
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Addis Ababa University
Abstract
For mobile operators, delivering quality service to their customers is very important as service
quality significantly affects their business. To achieve a consistent quality service delivery, they
need to continuously monitor and analyze their network performance and timely address any
obtained performance drops. Performance drops in mobile networks can be observed in key
performance indicators (KPIs) of spatially distributed cells with different magnitude and at
different time periods. As it is practically difficult to address all performance drops simultaneously,
it is preferable to make prioritized corrective actions starting from cells with critical drops, also
called anomaly cells. To detect anomaly cells, different automated methodologies have been
proposed and analyzed.
Yet, ethio telecom still applies manual and subjective anomaly detection method where measured
KPI values are manually compared with fixed thresholds to determine if the measured values are
within defined required ranges or not. Cells with KPI values out of the required range are analyzed
for identifying performance drop root causes and taking corrective actions. The manual and
subjective anomaly detection method is prone to detection errors and is maintenance time,
manpower and then cost inefficient. These challenges of manual and subjective detection can be
improved by applying advanced automatic methods based on machine learning algorithms.
In this thesis work, KNN based anomaly detection algorithms such as KNN classification, local
outlier factor (LOF) and connectivity outlier factor (COF) anomaly detection models is
implemented, and their comparative evaluation are made for Addis Ababa LTE cells. The
comparison is made based on type of output, complexity and their true positive rate (TPR) for time
series and cell level detections. Unlike KNN classification, LOF and COF do not need heavy data
set training and are able to provide anomaly scores instead of two-class labels. Experimentation
results show that COF provides slightly better performance than the other models with negligible
performance difference. For instance, the performance of COF with respect to TPR for RRC setup
success rate in the experimentation is 97.91% for cell level detection and 88% for time series
detection.
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Keywords
Anomaly detection, Connectivity outlier factor, Local outlier factor, KNN classification, Machine Learning, KPI, Alarm, LTE