A case study: Failure prediction in a real LTE network

Mobile traffic and number of connected devices have been increasing exponentially nowadays, with customer expectation from mobile operators in term of quality and reliability is higher and higher. This places pressure on operators to invest as well as to operate their growing infrastructures. As such, telecom network management becomes an essential problem. To reduce cost and maintain network performance, operators need to bring more automation and intelligence into their management system. Self-Organizing Networks function (SON) is an automation technology aiming to maximize performance in mobility networks by bringing autonomous adaptability and reducing human intervention in network management and operations. Three main areas of SON include self-configuration (auto-configuration when new element enter the network), self-optimization (optimization of the network parameters during operation) and self-healing (maintenance). The main purpose of the thesis is to illustrate how anomaly detection methods can be applied to SON functions, in particularly self-healing functions such as fault detection and cell outage management. The thesis is illustrated by a case study, in which the anomalies - in this case, the failure alarms, are predicted in advance using performance measurement data (PM data) collected from a real LTE network within a certain timeframe. Failures prediction or anomalies detection can help reduce cost and maintenance time in mobile network base stations. The author aims to answer the research questions: what anomaly detection models could detect the anomalies in advance, and what type of anomalies can be well-detected using those models. Using cross-validation, the thesis shows that random forest method is the best performing model out of the chosen ones, with F1-score of 0.58, 0.96 and 0.52 for the anomalies: Failure in Optical Interface, Temperature alarm, and VSWR minor alarm respectively. Those are also the anomalies can be well-detected by the model

SON: PREDICTING THE NATURE OF SERVICE DISRUPTIONS IN CELLULAR NETWORKS

An important aspect of communication is involved in its cellular network. To meet the demands, communication requires the next generation cellular network, i.e., self organizing networks (SON). In order to implement a self-organizing network, its subsections have to be known and optimized using certain rules. The objective of this document is to deal with one of the subsections called “Self-healing: Fault identification,” in particular by conducting analysis on the Telstra cellular network and predicting its disruptions. First, the prediction of the disruptions can be determined by establishing the machine learning algorithms upon Telstra data. Thus, the classification of faults could be used for finding the nature of the disruptions. Because the appropriate algorithm is chosen by the trial-and-error method, there is no one particular algorithm that fits particular data. Thus, data has to be pre-processed for the algorithms to be applied. Here, the Python Sci-kit module was used as a tool for developing the predictive model. As a note, there are many other tools like R, MATLAB, Rattle, KNIME, etc. that can be used for machine learning. Then, the nature of the faults was identified and investigated to drive customer advocacy

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future