Large-Scale Cell-Level Quality of Service Estimation on 5G Networks Using Machine Learning Techniques

TL;DR

This paper introduces a machine learning framework for estimating cell-level Quality of Service in 5G networks using traffic data, technical parameters, and network topology, enabling accurate predictions across different cities.

Contribution

It proposes novel feature engineering techniques and an end-to-end regression model for QoS estimation, improving prediction accuracy and generalization across diverse urban environments.

Findings

Effective QoS estimation across multiple cities

Enhanced prediction accuracy with engineered features

Robust model performance on unseen data

Abstract

This study presents a general machine learning framework to estimate the traffic-measurement-level experience rate at given throughput values in the form of a Key Performance Indicator for the cells on base stations across various cities, using busy-hour counter data, and several technical parameters together with the network topology. Relying on feature engineering techniques, scores of additional predictors are proposed to enhance the effects of raw correlated counter values over the corresponding targets, and to represent the underlying interactions among groups of cells within nearby spatial locations effectively. An end-to-end regression modeling is applied on the transformed data, with results presented on unseen cities of varying sizes.