Performance Tuning of 5G Networks Using AI and Machine Learning Algorithms

Abstract

As the demand for faster and more reliable mobile networks intensifies, the deployment of 5G has emerged as a transformative solution to meet the growing needs of connectivity. However, to fully leverage the potential of 5G networks, it is crucial to optimize their performance. This paper explores the application of Artificial Intelligence (AI) and Machine Learning (ML) algorithms in the performance tuning of 5G networks, focusing on areas such as network resource allocation, traffic prediction, and real-time decision-making. By analyzing vast datasets generated by 5G infrastructures, AI and ML enable dynamic adjustments, thereby improving network efficiency, reducing latency, and enhancing overall user experience. The integration of these advanced technologies allows for self-optimizing networks that adapt in real time, minimizing human intervention and operational costs. This research highlights the key algorithms and techniques used for performance optimization, discusses the challenges of implementing AI in real-world 5G networks, and outlines the future directions for achieving fully autonomous network management. Ultimately, the study illustrates how AI and ML are pivotal in driving the future of telecommunications through intelligent 5G network tuning.