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

arXiv: https://arxiv.org/abs/2212.14071 · HTML: https://mlsanity.com/paper/2212.14071/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 28 Dec 2022.

## Builds on (most cited first, 7 of 7)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [CatBoost: gradient boosting with categorical features support](https://mlsanity.com/paper/1810.11363/) (2018)
- [Delving into Deep Imbalanced Regression](https://mlsanity.com/paper/2102.09554/) (2021)
- [A better measure of relative prediction accuracy for model selection and model estimation](https://mlsanity.com/paper/2105.05249/) (2021)
- [Machine Learning Based Mobile Network Throughput Classification](https://mlsanity.com/paper/2004.13148/) (2020)
- [Minimum Class Confusion based Transfer for Land Cover Segmentation in Rural and Urban Regions](https://mlsanity.com/paper/2212.02130/) (2022)
- [5G Long-Term and Large-Scale Mobile Traffic Forecasting](https://mlsanity.com/paper/2212.10869/) (2022)

## Abstract (arXiv)

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.