## 2 Review of Relevant Published Research
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref. No.** & **Models and Techniques** & **Regression/ Classification** & **Service/ Application** & **Dimensionality Reduction Methods** & **Performance** \\ \hline \multirow{4}{*}{[10]} & Neural Network (NN), & & & & Prediction accuracy (PA): \\  & Recurrent Neural Network & & & & - For NN: PA \(\pm\) 0.8127; \\  & (RNN) with Long Short-Term & & & (MD) algorithm & - For RN: PA \(\pm\) 0.8176; \\  & Memory (LSTM) neurons, & & & (MD) algorithm & - For RF: 0.7967; \\  & Random Forest (RF), & & & & - For SVM: PA \(\pm\) 0.8107. \\ \hline \multirow{4}{*}{[11]} & LSTM; & & & Best performance in both \\  & k-medoids classification, & & & & relative mean error and \\  & Epanechnikov Kerne, & & & & Cloud / Edge & relative standard deviation. \\  & Moving average functions & & & & - This methodology can reduce \\  & & & & & the mean error by 45\% (which \\  & & & & & achieves around half of \\  & & & & & the benchmark) \\ \hline \multirow{4}{*}{[12]} & \multirow{4}{*}{Mathematical models} & \multirow{4}{*}{Regression} & M2M uplink communication & & & Can reduce the mean uplink \\  & & & & & delay significantly below the \\  & & & & & minimum possible for a \\  & & & & & non-predictive resource \\  & & & & & allocation algorithm \\ \hline \multirow{4}{*}{[13]} & Logistic Regression (LR), SVM, & & & & The nested cross-validation \\  & Decision Tree (DT) & & & & performance of the SVM, DT, \\  & & & & Networks & & and LR models are \\  & & & & Networks & & 0.664 \(\pm\) 0.1005, \\  & & & & & 0.743 \(\pm\) 0.004\%, and \\  & & & & & 0.609 \(\pm\) 0.076\%, respectively \\ \hline \multirow{4}{*}{[14]} & Artificial Neural Networks, & & & & & \\  & Decision Tree, Ensemble & & & & & The highest accuracy of the \\  & modeling Bagging technique & & & & features & prediction is estimated at 90\% \\  & with a Decision Tree & & & & & \\ \hline \multirow{4}{*}{[15]} & Mathematical models, PPM, & & & & PPM is able to achieve notable \\  & virtual queues & & & & improvement in terms of \\  & & & & & invalid packet rates and \\  & & & & & goodups compared to \\  & & & & & Maximum Throughput (MT), \\  & & & & Proportional Fair (PE), \\  & & & & & Modified Lapsed Weighted \\  & & & & Delay First (MLWDF), and \\  & & & & Exponential-Proponential Fair (EXP-PF) and very low delays \\ \hline \multirow{4}{*}{[16]} & Mathematical models, PPM, & & & & Possibility of expired packets \\  & virtual queues & Regression & & Real time services & - & can be reduced by the \\  & & & & & proposed PPM \\ \hline \multirow{4}{*}{[17]} & Multivariate linear regression & & & & Plane function very well \\  & technique & & & & represents the dependence of \\  & & & & average delay on average \\  & & & & & reported Channel Quality \\  & & & & indicator (CQI) and the \\  & & & & total traffic \\ \hline \multirow{4}{*}{[18]} & Logistic regression, Random & & & & & Model misclassified 20\% of the \\  & forest, Light gradient-boosting & & & & tested samples \\ \cline{1-1}  & machine (LightGBM), & & & & \\ \cline{1-1}  & Ensemble & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of important criteria in relevant published research papers. * The number of predictors in LTE delay examination is optimized for the first time by simultaneously using three approaches for predictive modeling of delays in the LTE network;
* A complete set of 17 independent/input research variables is used and Dimensionality Reduction is explained in detail;
* The original indirect method of assessment and calculation of the values of the dependent/output variable is applied;
* The optimization of the set of input variables is modeled with Feature Selection techniques and the Pareto 80/20 rule, and the obtained results are compared according to the criteria of prediction accuracy and complexity/interpretability of the model.