A Survey of Predictive Maintenance and Self-optimization in Telecom Field Based on Machine Learning

The role of telecommunication and mobile networks changes from a medium that connects people through voice to a complex system that affects almost everything in our life. It is not just connecting people but also connecting things. It also acts as a medium for many services in almost all domains including health, education, security, and other domains. As telecom network grows in a complex way and our needs for a stable and high-quality service grow every day, there is a necessity to improve network and telecommunication services quality and ensure service availability 24/7. Nowadays we are also heading towards the industry 4.0 revolution, which revolutionizes maintenance, quality, and productivity. Network predictive maintenance is an important research field to take corrective actions that prevent network failure. In addition to that having autonomous network and telecommunication systems that could adapt to traffic changes and customer changing behavior becomes an important research field. Machine learning has always contributed to the implementation of predictive maintenance of telecom networks along with the autonomous self-adapted network. In this paper, we survey predictive maintenance in the telecommunication field and mobile networks. In addition to that, we survey network self-optimization through self-organizing networks, network slicing, and software-defined networks.