A survey of machine learning techniques applied to self-organizing cellular networks
In this paper, a survey of the literature of the past 15 years involving machine learning (ML)
algorithms applied to self-organizing cellular networks is performed. In order for future …
algorithms applied to self-organizing cellular networks is performed. In order for future …
Cell fault management using machine learning techniques
This paper surveys the literature relating to the application of machine learning to fault
management in cellular networks from an operational perspective. We summarise the main …
management in cellular networks from an operational perspective. We summarise the main …
Automatic root cause analysis for LTE networks based on unsupervised techniques
A Gómez-Andrades, P Munoz… - IEEE Transactions on …, 2015 - ieeexplore.ieee.org
The increase in the size and complexity of current cellular networks is complicating their
operation and maintenance tasks. While the end-to-end user experience in terms of …
operation and maintenance tasks. While the end-to-end user experience in terms of …
Detecting anomalies in cellular networks using an ensemble method
GF Ciocarlie, U Lindqvist, S Nováczki… - Proceedings of the …, 2013 - ieeexplore.ieee.org
The Self-Organizing Networks (SON) concept includes the functional area known as self-
healing, which aims to automate the detection and diagnosis of, and recovery from, network …
healing, which aims to automate the detection and diagnosis of, and recovery from, network …
CellPAD: Detecting performance anomalies in cellular networks via regression analysis
How to accurately detect Key Performance Indicator (KPI) anomalies is a critical issue in
cellular network management. We present CellPAD, a unified performance anomaly …
cellular network management. We present CellPAD, a unified performance anomaly …
Scaling deep learning models for spectrum anomaly detection
Spectrum management in cellular networks is a challenging task that will only increase in
difficulty as complexity grows in hardware, configurations, and new access technology (eg …
difficulty as complexity grows in hardware, configurations, and new access technology (eg …
Methodology for the design and evaluation of self-healing LTE networks
Self-healing networks aim to detect cells with service degradation, identify the fault cause of
their problem, and execute compensation and repair actions. The development of this type …
their problem, and execute compensation and repair actions. The development of this type …
Anomaly detection algorithms for the sleeping cell detection in LTE networks
The Sleeping Cell problem is a particular type of cell degradation in Long-Term Evolution
(LTE) networks. In practice such cell outage leads to the lack of network service and …
(LTE) networks. In practice such cell outage leads to the lack of network service and …
Semi-supervised machine learning aided anomaly detection method in cellular networks
The ever-increasing amount of data in cellular networks poses challenges for network
operators to monitor the quality of experience (QoE). Traditional key quality indicators (KQIs) …
operators to monitor the quality of experience (QoE). Traditional key quality indicators (KQIs) …
Root cause analysis based on temporal analysis of metrics toward self-organizing 5G networks
By 2020, mobile networks will support a wide range of communication services while at the
same time, the number of user terminals will be enormous. To cope with such increased …
same time, the number of user terminals will be enormous. To cope with such increased …