Machine Learning Approaches in 5G Networks

Abstract: As the deployment of 5G networks accelerates globally, these networks are set to revolutionize the telecommunications landscape by providing unprecedented data speeds, ultra-low latency, and massive connectivity for devices across diverse applications. However, the increasing complexity and dynamic nature of 5G networks necessitate advanced approaches to optimize performance and manage resources efficiently. Machine learning (ML) emerges as a pivotal technology capable of addressing these challenges through data-driven insights and autonomous decision-making processes. This paper delves into the integration of ML techniques within the 5G network infrastructure, exploring how they can be leveraged to enhance various network functionalities. We systematically analyze three primary categories of ML: supervised learning, unsupervised learning, and reinforcement learning, each offering unique capabilities to tackle distinct aspects of network management.