Paper Key : IRJ************192
Author: Harshal Vasant Borse
Date Published: 14 Oct 2023
Abstract
In this research, we analyze self-organized network management from the network's beginning to finish. Self-organization is commonly referred to as Self-organizing Networks (SONs) in cellular networks, and it is a crucial driver for enhancing Operations, Administration, and Maintenance (OAM) tasks. SON seeks to reduce the cost of 4G and future 5G network installation and administration by simplifying operational chores with the capacity to setup, optimize, and heal itself. This autonomous management vision must be extended throughout the end-to-end network to meet 5G network management needs. Machine Learning (ML) has been highlighted as the primary instrument to apply autonomous adaptation and take use of experience when making judgments in the literature and in certain instances of goods available on the market. In this article, we look at how machine learning technologies may help network management. We discuss and offer the fundamental principles and taxonomy for SON, network management, and machine learning. We examine the state of the art in the literature, standards, and the market. We pay special attention to the evolution of the Third Generation Partnership Project (3GPP) in network management, as well as the data that can be extracted from 3GPP networks, in order to gain knowledge and experience in how the network works, and to improve network performance in a proactive manner. Finally, we discuss the major issues connected with this area of study, both in 4G and in how 5G is being developed, while also highlighting new research avenues.
DOI LINK : 10.56726/IRJMETS45258 https://www.doi.org/10.56726/IRJMETS45258