Machine Learning Based on Exhaustive GMM Clustering Algorithm for Optimal Learning of 5G-NR SINR Datasets

Abstract

 The rapid evolution of 5G technology has ushered in a new era of unparalleled connectivity and data speeds, creating vast opportunities for innovation across industries. Central to the optimization of 5G networks is the analysis of (Signal-to-Interference-plus-Noise Ratio) quality datasets), which plays a crucial role in understanding network performance and enhancing user experience. In this context, the application of advanced clustering techniques, such as the Gaussian Mixture Model (GMM), offers a powerful framework for extracting valuable insights from 5G-NR SINR datasets. However, the optimal tuning of GMM parameters poses significant challenges that require innovative solutions. This paper explores an exhaustive GMM tuning algorithm designed to achieve optimal cluster learning of 5G-NR SINR datasets, providing a comprehensive analysis of methodologies, results, and implications for network optimization.