ОГЛЯД МЕХАНІЗМІВ ROOT CAUSE АНАЛІЗУ ТЕЛЕКОМУНІКАЦІЙНИХ МЕРЕЖ

Abstract

Telecommunication networks are an integral part of modern information society, providing data transmission and communication among millions of users worldwide. The complexity and scale of these networks are constantly growing, which increases the demands on their reliability and stability. One of the key tasks in ensuring the efficient operation of telecommunication networks is the identification and elimination of the root causes of failures and anomalies that can significantly affect the quality of service for end users. Root cause analysis (RCA) is a powerful tool for identifying the primary causes of problems, preventing their recurrence, and enhancing the overall reliability of networks. The article provides a detailed review of contemporary methods and tools of RCA used in telecommunication networks. In particular, methods such as fishbone diagram analysis, the «five whys» method, fault tree analysis, and the application of machine learning for big data analysis are considered. Each of these approaches has its advantages and disadvantages, and their comprehensive use allows for higher accuracy and efficiency in identifying the root causes of problems. Practical examples demonstrate the effectiveness of applying various RCA methods to solve specific problems in telecommunication networks. For instance, the use of fishbone diagram analysis and the «five whys» method allows for the identification of the primary causes of network delays and equipment issues at base stations. The implementation of failure prediction systems based on machine learning significantly enhances network reliability by allowing for the early detection of potential problems and the necessary technical measures. The prospects for the development of RCA in telecommunication networks are outlined. The primary focus is on the development of adaptive and self-learning systems capable of dynamically responding to changing network conditions. The use of more complex artificial intelligence algorithms and tools for predicting potential problems will significantly improve the effectiveness of RCA and ensure high-quality telecommunication services.