The increase in size and complexity of current cellular networks is complicating their operation and maintenance tasks. While the end-to-end user experience in terms of throughput and latency has been significantly improved, cellular networks have also become more prone to failures. In this context, mobile operators start to concentrate their efforts on creating Self-Healing networks, i.e. those networks capable of performing troubleshooting in an automatic way, making the network more reliable and reducing costs. In this paper, an automatic diagnosis system based on unsupervised techniques for Long-Term Evolution (LTE) networks is proposed. In particular, this system is built through an iterative process, using SelfOrganizing Maps (SOM) and the Ward's Hierarchical method, in order to guarantee the quality of the solution. Furthermore, in order to obtain a number of relevant clusters and label them properly from a technical point of view, an approach based on the analysis of the statistical behaviour of each cluster is proposed. Moreover, with the aim of increasing the accuracy of the system, a novel adjustment process is presented. It intends to refine the diagnosis solution provided by the traditional SOM according to the so-called Silhouette index and the most similar cause on the basis of the minimum X th percentile of all distances. The effectiveness of the developed diagnosis system is validated using real and simulated LTE data by analysing its performance and comparing it with reference mechanisms.
Self-Organizing Networks (SON) mechanisms reduce Operational Expenditure (OPEX) in cellular networks, whilst enhancing the offered quality of service. Within 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.
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