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This study presents a general machine learning framework to estimate thetraffic-measurement-level experience rate at given throughput values in theform of a Key Performance Indicator for the cells on base stations acrossvarious cities, using busy-hour counter data, and several technical parameterstogether with the network topology. Relying on feature engineering techniques,scores of additional predictors are proposed to enhance the effects of rawcorrelated counter values over the corresponding targets, and to represent theunderlying interactions among groups of cells within nearby spatial locationseffectively. An end-to-end regression modeling is applied on the transformeddata, with results presented on unseen cities of varying sizes.

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