Pedro Lázaro

Automated Diagnosis for UMTS Networks Using Bayesian Network Approach

This paper presents an automated diagnosis in troubleshooting (TS) for Universal Mobile Telecommunications System (UMTS) networks using a Bayesian network (BN) approach. An automated diagnosis model is first described using the Naive Bayesian Classifier. To increase the performance of the diagnosis model, the entropy minimization discretization (EMD) method is incorporated into the model to select optimal segments for the discretization of the input symptoms. In the first phase, the diagnosis model is constructed using a dynamic simulator. The simulator TS platform allows generation of a large amount of data required to study the relations between faults and symptoms. In the second phase, the diagnosis model is adapted to a real UMTS network using counters and key performance indicators (KPIs) recovered from an Operations and Maintenance Center (OMC). Results for the automated diagnosis using both network simulator and real UMTS network measurements illustrate the efficiency of the proposed TS approach and its importance to mobile network operators.

A unified framework for self-healing in wireless networks

Future wireless networks will be Heterogeneous Networks (HetNet), as they will comprise multiple Radio Access Technologies (RAT) and network architectures. The complexity of managing and operating such networks is forcing operators to find new strategies if they want to remain competitive. In this context, Self-Organizing Networks (SON) have arisen as one of the key technologies for enhancing operation and minimizing costs in future networks. SON functionalities can be classified as self-configuration, self-optimization, and self-healing. Although the first two categories have received considerable attention in the last years, the studies devoted to self-healing have been scarce. In addition, existing references use different terminology and concepts and they focus only on partial aspects of self-healing for a particular RAT. This article unifies previous research and proposes a reference model for self-healing in which terms and functions are unambiguously defined. In addition, this article presents a survey on the state of the art, in which the main references on self-healing are outlined. Finally, the main research challenges in self-healing that will have to be faced in the near future are summarized.

Continuous versus Discrete Model in Autodiagnosis Systems for Wireless Networks

In the near future, several radio access technologies will coexist in Beyond 3G mobile networks (B3G), and they will be eventually transformed into one seamless global communication infrastructure. Self-managing systems (i.e., those that self-configure, self-protect, self-heal, and self-optimize) are the solution to tackle the high complexity inherent to these networks. In this context, this paper proposes a system for autodiagnosis in the Radio Access Network (RAN) of wireless systems. The malfunction of the RAN may be due not only to a hardware fault but also (and more difficult to identify) to a bad configuration. The proposed system is based on the analysis of Key Performance Indicators (KPIs) in order to isolate the cause of the network malfunction. In this paper, two alternative probabilistic systems are compared, which differ on how KPIs are modeled (continuous or discrete variables). Experimental results are examined in order to support the theoretical concepts, based on data from a live network. The drawbacks and benefits of both systems are studied, and some conclusions on the scenarios under which each model should be used are presented.

Knowledge acquisition for diagnosis model in wireless networks

In the near future, several radio access technologies will coexist in Beyond 3G mobile networks (B3G) and they will be eventually transformed into one seamless global communication infrastructure. Self-managing systems (i.e. those that self-configure, self-protect, self-heal and self-optimize) are the solution to tackle the high complexity inherent to these networks. This paper proposes a probabilistic model for self-healing in the radio access network (RAN) of wireless systems. The main difficulty in model construction is that, contrary to other application domains, in wireless networks there are no databases of previously classified cases from which to learn the model parameters. Due to this reason, in this paper, a knowledge acquisition procedure is proposed to build the model from the knowledge of troubleshooting experts. In order to support the theoretical concepts, a model has been built and it has been tested in a live network, proving the feasibility of the proposed system. Additionally, a knowledge-based model has been compared to a data-based model, showing the benefits of the former when the number of training cases is scarce.

Design of a Computationally Efficient Dynamic System-Level Simulator for Enterprise LTE Femtocell Scenarios

In the context of Long-Term Evolution (LTE), the next generation mobile telecommunication network, femtocells are low-power base stations that efficiently provide coverage and capacity indoors. This paper presents a computationally efficient dynamic system-level LTE simulator for enterprise femtocell scenarios. The simulator includes specific mobility and traffic and propagation models for indoor environments. A physical layer abstraction is performed to predict link-layer performance with low computational cost. At link layer, two important functions are included to increase network capacity: Link Adaptation and Dynamic Scheduling. At network layer, other Radio Resource Management functionalities, such as Admission Control and Mobility Management, are also included. The resulting tool can be used to test and validate optimization algorithms in the context of Self-Organizing Networks (SON).

Comparison of probabilistic models used for diagnosis in cellular networks

In the forthcoming years, different radio access technologies (GSM, GPRS, UMTS, etc.) will have to coexist within the same cellular network. In this scenario of increasingly complex networks, automated management is becoming a crucial issue to provide high-quality services. In this paper, a system for automatic fault diagnosis of the radio access part of a mobile communication system is presented. For this purpose, a probabilistic diagnosis model based on discrete Bayesian networks (BNs) is proposed. There is always a trade-off between accuracy and complexity of the model. Hence, two alternative structures to code the dependencies among elements in the model are compared with regard to their simplicity and performance. Empirical results are examined, based on data from a live GSM/GPRS network. Taking into account the experiments, a BN structure is selected for diagnosis in cellular networks

Multiple intervals versus smoothing of boundaries in the discretization of performance indicators used for diagnosis in cellular networks

Most real-world applications of diagnosis involve continuous-valued attributes, which are normally discretized before the existing classification algorithms are applied. The discretization may be based on data or on human expertise. In cellular networks the number of classified examples is very limited. Thus, the diagnosis experts should specify the boundaries of the intervals for each discretized symptom. The large number of values makes it difficult to specify precise parameters. Even if boundaries are obtained from classified examples, due to the limited number of cases, the obtained values are not very accurate. In this paper two techniques to improve the performance of diagnosis systems based on Bayesian Networks are compared. Some empirical results are presented for diagnosis in a GSM network. The first method, Smooth Bayesian Networks, is shown to be more robust to imprecise setting of boundaries. The second method, Multiple Uniform Intervals, is superior if accurately defined boundaries are available.

Automated Troubleshooting of Satellite Communication Ground Equipment

This paper presents the first, very promising results of the automated troubleshooting of satellite communication ground equipment (ATSIG) project, which is a research project conducted by Wirtek and University of Malaga and supported by European Space Agency. The project develops a novel concept for automating the troubleshooting process of a Satcom network operator. The work presented contains a thorough description of the developed concept, which is based on advanced technology from the artificial intelligence domain (Bayesian networks). This technology is ideal for solving the very complex problem of producing a diagnosis automatically in a domain with a lot of uncertainty. Although the presented work is specific to Satcom troubleshooting, the approach and results are immediately applicable for similar complex diagnostic domains.