Emil J. Khatib

Data mining for fuzzy diagnosis systems in LTE networks

A Knowledge Acquisition learning algorithm is proposed for troubleshooting in LTE.A sensitivity analysis is performed on the proposed algorithm.The algorithm is tested with a live network scenario.The performance has been compared with a Bayesian Network based algorithm. The recent developments in cellular networks, along with the increase in services, users and the demand of high quality have raised the Operational Expenditure (OPEX). Self-Organizing Networks (SON) are the solution to reduce these costs. Within SON, self-healing is the functionality that aims to automatically solve problems in the radio access network, at the same time reducing the downtime and the impact on the user experience. Self-healing comprises four main functions: fault detection, root cause analysis, fault compensation and recovery. To perform the root cause analysis (also known as diagnosis), Knowledge-Based Systems (KBS) are commonly used, such as fuzzy logic. In this paper, a novel method for extracting the Knowledge Base for a KBS from solved troubleshooting cases is proposed. This method is based on data mining techniques as opposed to the manual techniques currently used. The data mining problem of extracting knowledge out of LTE troubleshooting information can be considered a Big Data problem. Therefore, the proposed method has been designed so it can be easily scaled up to process a large volume of data with relatively low resources, as opposed to other existing algorithms. Tests show the feasibility and good results obtained by the diagnosis system created by the proposed methodology in LTE networks.

Diagnosis Based on Genetic Fuzzy Algorithms for LTE Self-Healing

Self-organizing network (SON) mechanisms reduce operational expenditure in cellular networks while enhancing the offered quality of service. Within a SON, self-healing aims to autonomously solve problems in the radio access network and to minimize their impact on the user. Self-healing comprises automatic fault detection, root cause analysis, fault compensation, and recovery. This paper presents a root cause analysis system based on fuzzy logic. A genetic algorithm is proposed for learning the rule base. The proposed method is adapted to the way of reasoning of troubleshooting experts, which ease knowledge acquisition and system output interpretation. Results show that the obtained results are comparable or even better than those obtained when the troubleshooting experts define the rules, with the clear benefit of not requiring the experts to define the system. In addition, the system is robust, since fine tuning of its parameters is not mandatory.

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 of automatic system presents several challenges to be confronted. The first challenge is the scarce number of historically reported faults, which greatly complicates the evaluation of novel self-healing techniques. For this reason, in this paper, a system model to simulate faults in Long-Term Evolution (LTE) networks, along with their most significant key performance indicators, is proposed. Second, the expert knowledge required to build a self-healing system is usually not documented. Therefore, in this paper, a methodology to extract this information from a collection of reported cases is proposed. Finally, following the proposed methodology, an automatic fuzzy-logic-based system for fault identification in LTE networks is designed. Evaluation results show that the fuzzy system provides fault identification with a high success rate.

Self-healing in mobile networks with big data

Mobile networks have rapidly evolved in recent years due to the increase in multimedia traffic and offered services. This has led to a growth in the volume of control data and measurements that are used by self-healing systems. To maintain a certain quality of service, self-healing systems must complete their tasks in a reasonable time. The conjunction of a big volume of data and the limitation of time requires a big data approach to the problem of self-healing. This article reviews the data that self-healing uses as input and justifies its classification as big data. Big data techniques applied to mobile networks are examined, and some use cases along with their big data solutions are surveyed.

LTE performance data reduction for knowledge acquisition

In the last years, mobile networks have seen a great increase in complexity, as the data traffic, the demand for quality and the variety of offered services have grown. The management costs of modern networks are growing, at the same time as operators compete to offer shorter downtime and less impact of network issues on the user experience. Self-Organizing Networks (SON) offer a solution to these problems. Among these SON functionalities in cellular networks, self-healing automates the resolution of problems in the radio access network. To perform the task of diagnosis (or root cause analysis), Knowledge-Based Systems (KBS) are often used. These systems need a previous process of training (or learning), in which they are fed instances of real problems. In this paper, an algorithm for extracting the key information for these vectors is proposed. The inputs of the algorithm are big matrices of time-dependent network performance data, and the outputs are simple one-dimensional vectors ready to be used in learning algorithms.

Knowledge Acquisition for Fault Management in LTE Networks

Self-healing is one of the main functionalities of Self-Organizing-Networks. Among self-healing functions, diagnosis or root cause analysis, consisting of identifying the fault cause in problematic cells, is one of the most complex tasks. Expert systems, such as Fuzzy Logic Controllers or Bayesian Networks, have been previously proposed to implement automatic diagnosis systems in the radio access segment of mobile communication networks. In order to achieve accurate results, these diagnosis systems should contain the knowledge of experienced LTE troubleshooting experts. However, these experts normally have neither the time nor the expertise in artificial intelligence to define the expert system. In this work, we propose a novel knowledge acquisition system that obtains this knowledge in the least possible intrusive way. The proposed method collects the Performance Indicators data from the relevant time intervals together with the expert’s diagnosis and uses them as inputs for a Data Mining algorithm to extract diagnosis rules.

Modelling LTE Solved Troubleshooting Cases

Abstract Self-Organizing Networks (SON) add automation to the Operation and Maintenance of mobile networks. Self-healing is the SON function that performs automated troubleshooting. Among other functions, self-healing performs automatic diagnosis (or root cause analysis), that is the task of identifying the most probable fault causes in problematic cells. For training the automatic diagnosis functionality based on support-decision systems, supervised learning algorithms usually extract the knowledge from a training set made up from solved troubleshooting cases. However, the lack of these sets of real solved cases is the bottleneck in the design of realistic diagnosis systems. In this paper, the properties of such troubleshooting cases and training sets are studied. Subsequently, a method based on model fitting is proposed to extract a statistical model that can be used to generate vectors that emulate the network behavior in the presence of faults. These emulated vectors can then be used to evaluate novel diagnosis systems. In order to evaluate the feasibility of the proposed approach, an LTE fault dataset has been modeled, based on both the analysis of real cases collected over two months and a network simulator. In addition, the obtained baseline model can be very useful for the research community in the area of automatic diagnosis.

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 complexity in network management, innovative solutions for the next generation of self-organizing networks (SONs) need to be deployed. In the field of self-healing, the heterogeneous character of future fifth-generation (5G) radio access networks (RANs) will provide a diversity of measurements and metrics that can be translated into valuable knowledge to support detection and diagnosis activities. The more complete the information gathered, the better the SON mechanisms will be able to effectively analyze and solve radio problems. However, temporal dependence between metrics has not been previously addressed in the literature. In this paper, a self-healing method based on network data analysis is proposed to diagnose problems in future RANs. The proposed system analyzes the temporal evolution of a plurality of metrics and searches for potential interdependence under the presence of faults. Performance is evaluated with real data from a mature Long-Term Evolution (LTE) network. Results show that the proposed method exploits the available data in the context of heterogeneous scenarios, reducing the diagnosis error rate.

Degradation Detection Algorithm for LTE Root Cause Analysis

Self-Organizing Networks (SON) aim to automate network Operation & Maintenance tasks. SONs comprise self-configuration, self-optimization and self-healing. Within self-healing, root cause analysis, i.e. diagnosis of the cause of the network problems, is one of the most difficult tasks. To automate diagnosis, Data Mining (DM) algorithms over sets of solved troubleshooting cases can be applied in order to use Knowledge Based Systems. Data reduction is part of the DM process, where large time-dependent matrices of Performance Indicators (PIs) are transformed into time-independent vectors of values. In this work, an algorithm for data reduction is proposed, which is based on detecting the time intervals when the service of an LTE eNodeB is degraded and aggregating the values of the time dependent PIs for those intervals. The results show that the detecting capability of the algorithm is higher than other proposed solutions, and that a high volume reduction factor can be achieved.