Deep Learning-based Cell Performance Degradation Prediction
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Date
2022-07
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Addis Ababa University
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.
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Keywords
Performance degradation detection, Multivariate forecasting, LSTM, CNNLSTM, ConvLSTM, Deep Learning Network, UMTS, KPI