## 5 Results and Discussion
### Multilayer Perceptron Model (MLP)  
The proposed MLP model is a supervised machine learning model that, by training on a training data set, learns the function that performs mapping \(R^{m}\to R^{o}\). In this case, the dimension of the input space is \(m=17\), and the output \(o\) = 2. According to the algorithm given in Section 3, the first step in creating an MLP model in the IBM SPSS technology involves preprocessing the data, i.e., rescaling or reducing all input values to the same range. In this study, a standardization method was used according to which the variables are rescaled binary so that they have an arithmetic mean equal to 0 and a standard deviation equal to 1. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dependent Variables**} & **Cell Downlink Average Throughput (\(Y_{1}\))** & \multicolumn{3}{c}{**Average User Downlink Throughput (\(Y_{2}\))**} \\ \cline{2-7}  & **Unstandardized Coefficients** & \multirow{2}{*}{**Sig.**} & \multicolumn{3}{c}{**Unstandardized Coefficients**} \\ \cline{2-7}  & **B** & **Std. Error** & & **B** & **Std. Error** & \\ \hline (Constant) & \(-922.443\) & 214.142 & 0.000 & \(-11.194\) & 0.358 & 0.000 \\ DL PRB Usage Rate (\(X_{1}\)) & \(-416.156\) & 4.679 & 0.000 & \(-0.941\) & 0.008 & 0.000 \\ Average CQ (\(X_{2}\)) & 1359.353 & 19.397 & 0.000 & 3.261 & 0.032 & 0.000 \\ RRC.ConvReg.Succ (\(X_{3}\)) & 0.428 & 0.033 & 0.000 & 0.001 & 0.000 & 0.000 \\ Traffic.User.Avg (\(X_{4}\)) & \(-22.48\) & 5.621 & 0.000 & \(-0.103\) & 0.009 & 0.000 \\ DL ReTrans Rate (\(X_{5}\)) & \(-289.941\) & 11.376 & 0.000 & 0.202 & 0.019 & 0.000 \\ DL iBLER (\(X_{6}\)) & 748.020 & 12.815 & 0.000 & 0.046 & 0.021 & 0.033 \\ DL Cell Traffic Volume (\(X_{7}\)) & 1043.475 & 6.101 & 0.000 & 1.031 & 0.010 & 0.000 \\
16 QAM.ErrTB.Ibler (\(X_{8}\)) & \(-0.157\) & 0.005 & 0.000 & \(-7.331\times 10^{-5}\) & 0.000 & 0.000 \\
64 QAM.ErrTB.Ibler (\(X_{9}\)) & \(-0.046\) & 0.003 & 0.000 & \(7.704\times 10^{-5}\) & 0.000 & 0.000 \\ QPSK.ErrTP.Ibler (\(X_{10}\)) & 0.033 & 0.003 & 0.000 & \(4.820\times 10^{-5}\) & 0.000 & 0.000 \\
16 QAM.TB.Retrans (\(X_{11}\)) & 0.075 & 0.004 & 0.000 & \(1.603\times 10^{-5}\) & 0.000 & 0.008 \\
64 QAM.TB.Retrans (\(X_{12}\)) & 0.016 & 0.002 & 0.000 & \(-4.992\times 10^{-5}\) & 0.000 & 0.000 \\ QPSK.TB.Retrans (\(X_{13}\)) & \(-0.025\) & 0.002 & 0.000 & \(-1.762\times 10^{-5}\) & 0.000 & 0.000 \\
16 QAM.TB (\(X_{14}\)) & 0.004 & 0.000 & 0.000 & \(1.695\times 10^{-6}\) & 0.000 & 0.001 \\
64 QAM.TB (\(X_{15}\)) & \(-0.005\) & 0.000 & 0.000 & \(-8.159\times 10^{-6}\) & 0.000 & 0.000 \\ QPSK.TB (\(X_{16}\)) & 0.003 & 0.000 & 0.000 & \(1.049\times 10^{-5}\) & 0.000 & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Basic results of the regression analysis. In order to select the best final model, a total of 30 variants of the MLP model were tested for prediction of the observed dependent variables with different combinations of ratios of the set sizes for training and testing (30%:70%, 40%:60%, 50%:50%, 60%:40%, 70%:30%), and different values of the following metaparameters:  
(1) _Number of neurons in hidden layers_. The architecture of all tested model variants consists of one input layer with 17 neurons, one output layer with two neurons and two hidden layers between them, whose size varies in combinations and can be: 10,10; 20,20; 30,30. (2) _The shape of the output function of neurons in hidden layers_. The IBM SPSS technology offers the possibility to select one of the two functions, which were examined in combinations with the first metaparameter of the model, namely hyperbolic tangent (_Hyp. Tg._) and sigmoid function (_Sig._). Each of the 30 variants of the MLP model with the above combinations were tested by multiple passes through the multilayer network architecture, i.e., triple consecutive trainings and tests, and the results are presented by the relative testing errors in Table 7 for both dependent variables.