ML based performance assurance and VoC management of highly convergence mobile operator network.
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Date
2023-10Publisher
Brac UniversityAuthor
Rahman, Md. ArifurMetadata
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The convergence of technology in mobile operator networks has ushered in a new
era of interconnectedness and communication efficiency. As these networks become
increasingly complex, ensuring their optimal performance and addressing customer
concerns become paramount. This thesis delves into the realm of ”ML Based Performance
Assurance and VoC Management of Highly Convergence Mobile Operator
Network,” presenting a multifaceted approach to tackling these challenges through
advanced machine learning (ML) techniques by obsolete traditional manual working
approach of MNOs.Within the scope of ML-based performance assurance, this
study places a strong emphasis on Forecasting Time Series and detecting Anomalies.
Leveraging the power of predictive analytics, the research harnesses a spectrum of
algorithms including ARIMA, XGBoost, LSTM, Dynamic Linear Model, Prophet,
VAR, and GRU. This enables the anticipation of network behavior and facilitates
proactive measures for optimization. Additionally, the study integrates sophisticated
Anomaly Detection methods encompassing DBSCAN, Isolation Forest, Local Outlier
Factor (LOF), One Class SVM, Elliptic Envelope, and Autoencoders. These
techniques empower the system to identify and mitigate aberrations in real-time,
safeguarding network statistics and ensure business growth. Extending the purview
of the research, the study delves into Voice of Customer (VoC) Management within
the context of highly converged mobile operator networks. By employing diverse
algorithms such as SVM, CNN, GNB, MNB, and LR, the research addresses the
critical task of understanding customer insights, preferences,thoughts and concerns.
Through effective VoC analysis, operators can tailor their services to meet customer
expectations, thereby enhancing overall satisfaction. This thesis contributes to the
field by providing a all-encompassing structure for the enhancement and design of
mobile operator networks. The amalgamation of ML-based performance assurance
and VoC management techniques presents a holistic solution for network operators
and service providers. By proactively forecasting network behavior and promptly
addressing anomalies, operators can ensure seamless operations. Simultaneously,
a holistic and customer centric approach driven by advanced ML algorithms enables
the refinement of services based on customer feedback to obsolete traditional
working approach of mobile network operators.