Analyzing and optimizing 5G network with Machine Learning

Abstract

The rapid expansion of 5G networks necessitates the development of advanced
techniques to address the challenges associated with their deployment, optimization, and
maintenance. This paper explores the integration of machine learning (ML) algorithms for the
analysis and optimization of 5G network performance. By leveraging ML's capabilities in datadriven decision-making, we demonstrate how it can be applied to improve several aspects of 5G
networks, such as traffic management, resource allocation, interference mitigation, and fault
detection. The study evaluates a variety of ML models, including supervised and unsupervised
learning techniques, reinforcement learning, and deep learning approaches, to optimize key
network parameters such as throughput, latency, and coverage. Specific attention is given to the
role of ML in network slicing, user behavior prediction, dynamic spectrum management, and
energy efficiency, all critical elements of 5G networks. The proposed methodology involves the
collection and analysis of real-time network data, followed by the training of machine learning
models to predict network performance and optimize configurations based on observed trends. The
results of this study highlight the effectiveness of ML in achieving better network optimization,
improving Quality of Service (QoS), and reducing operational costs. Additionally, the paper
discusses the future potential of integrating ML with emerging technologies such as edge
computing and network automation to further enhance 5G network performance and sustainability.
This research provides valuable insights for network operators and engineers aiming to optimize
5G infrastructure and ensure robust, efficient, and sustainable network performance.