A novel heterogenous ensemble theory for symmetric 5G cells segmentation: Intelligent RAN analytics

Abstract

MNOs are investing more in 5G, rolling out sites in urban and specific rural areas. Meanwhile, it remains imperative to consistently maintain the network performance above a certain threshold for optimal user experience. Symmetric network cells characterized by parallel attributes in terms of capacity and coverage are instrumental in planning, optimization, and resource allocation. However, the variation in environmental factors introduces divergence in network cells' behavior, symmetric or not. Therefore, the need arises for intelligent analytic processes within the RAN system to categorize symmetric cells based on their performance and behavior. Intelligent optimization and analytics in 5G rely on the accurate and automated identification of cells exhibiting symmetric behavior, enabling bulk optimization operations. In this paper, we develop and assess a clustering approach using a heterogenous ensemble method to group 5G cells based on their key performance attributes to facilitate network optimization tasks. The approach involves a synergistic integration of K-means and hierarchical clustering algorithms, enabling dynamic segmentation of cells based on their performance behavior. Leveraging the clustering output, we train an XGBoost classifier, paving the way for a comprehensive analytics framework and problematic or poor-performing cells’ detection. We apply the study model to real-world 5G RAN metrics and evaluate the proposed method in terms of clustering accuracy and convergence. The study output showcases the efficacity of the heterogenous ensemble approach compared to individual clustering algorithms, providing a valuable baseline for network performance enhancement. With such a dynamic approach for analyzing 5G new radio (NR) performance, MNOs can move toward intelligent and self-aware networks, making informed decisions regarding resource allocation and coverage optimization.