Large-Scale Cell-Level Quality of Service Estimation on 5G Networks Using Machine Learning Techniques
This addresses quality of service estimation for 5G network operators, but appears incremental as it applies existing machine learning techniques to a new domain.
The study tackled the problem of estimating traffic-measurement-level experience rates as a Key Performance Indicator for cells on 5G base stations using machine learning, achieving results presented on unseen cities of varying sizes.
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.