Artificial Intelligence and Automation on Tackle the 5G Network’s Complexity with Edge Computing Integration

The massive influx of new connected devices and the unique requirements needed for the growth of new applications make the deployment of 5G networks far more challenging than before. This problem requires that AI and automation methodologies be brought together in order to address the complexity of the issue. In view of these entailing features of 5G networks, this research work is centered on edge computing as the enabling platform for AI and automation. There are two principal advantages connected with the usage of edge computing: the observed decrease in latency and the opportunity to make decisions in real time. To face the highly dynamic conditions in 5G networks, operators are now implementing AI algorithms and automation mechanisms to the network’s edge. Judgments based on context and administrative work can now be done on this network, owe to this integration.In this work, we explore edge AI and automation such as machine learning, deep learning, or prediction models. These methods help in real time data processing, network optimization and resource management in smart and efficient manner. However, 5G Networks in utilizing of AI and automation can decide on intelligent basis, the behavior pattern of the network and the requirement of the users through the large amount of data generated at edge. AI and automation have many benefits when applied to 5G networks including the improvement of the speed of networks, quality of offered services and improved usage of resources. The report also covers such concerns as privacy concerns, susceptibility to security threats, and resource constraints as AI and automation find their place at the edge. The research shows that in order to overcome the barriers associated with 5G networks, it is necessary to integrate artificial intelligence and automation with edge computing. The presented integration possibilities are explained, and the need for the further development of AI algorithms and edge computing infrastructure as well as the introduction of unified interfaces is discussed. Edge computing can bring hope of the intelligent and efficient management of 5G networks to make way for the widespread rollout of a variety of new services and applications when integrated with AI and automation.