# A Novel Link-to-System Mapping Technique Based on Machine Learning for 5G/IoT Wireless Networks
## 5 Numerical Results and Analysis
### Simulation Validation  
Figures 7 and 8 show the results of effective SNR mapping by the proposed ML-based EESM method in the case of 5G CDL-A SISO. \"CQI1_SNR_k\", \"CQI5_SNR_k\", \"CQI10_SNR_k\", and \"CQI15_SNR_k\" indicate the post-processing SINRs \(sint_{k}\), as mentioned in Equation (6). Since the number of allocated REs \(N_{RE}\) was 300 (25 RB \(\times\) 12 sub-carriers \(\times\) 1 symbol time = 300 REs),  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**CQI**} & \multicolumn{3}{c}{**AdaGrad**} & \multicolumn{3}{c}{**RMSProp**} \\ \cline{2-7}  & \(\mathbf{\alpha_{1}}\) & \(\mathbf{\alpha_{2}}\) & **MSE** & \(\mathbf{\alpha_{1}}\) & \(\mathbf{\alpha_{2}}\) & **MSE** \\ \hline
1 & 3.294 & 3.230 & 0.150 & 2.752 & 2.698 & 0.149 \\
2 & 1.874 & 1.880 & 0.357 & 2.163 & 2.170 & 0.364 \\
3 & 1.607 & 1.594 & 0.065 & 2.002 & 1.988 & 0.061 \\
4 & 1.184 & 1.175 & 0.159 & 1.162 & 1.154 & 0.156 \\
5 & 1.286 & 1.283 & 0.140 & 1.552 & 1.546 & 0.206 \\
6 & 1.359 & 1.359 & 0.055 & 1.360 & 0.360 & 0.056 \\
7 & 3.642 & 3.628 & 0.170 & 3.643 & 3.629 & 0.170 \\
8 & 3.256 & 3.228 & 0.171 & 3.937 & 3.911 & 0.155 \\
9 & 5.563 & 5.543 & 0.110 & 5.636 & 5.616 & 0.110 \\
10 & 16.259 & 16.204 & 0.075 & 16.262 & 16.208 & 0.075 \\
11 & 13.685 & 13.604 & 0.329 & 13.382 & 13.301 & 0.268 \\
12 & 17.988 & 18.079 & 0.778 & 17.547 & 17.632 & 0.724 \\
13 & 23.971 & 23.970 & 0.555 & 24.112 & 24.111 & 0.558 \\
14 & 29.306 & 29.205 & 0.210 & 29.306 & 29.204 & 0.210 \\
15 & 33.590 & 33.833 & 0.533 & 28.733 & 28.739 & 0.823 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Optimal parameters \((\alpha_{1}^{*},\alpha_{2}^{*})\) in 5G CDL-A SISO.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**CQI**} & \multicolumn{3}{c}{**AdaGrad**} & \multicolumn{3}{c}{**RMSProp**} \\ \cline{2-7}  & \(\mathbf{\alpha_{1}}\) & \(\mathbf{\alpha_{2}}\) & **MSE** & \(\mathbf{\alpha_{1}}\) & \(\mathbf{\alpha_{2}}\) & **MSE** \\ \hline
1 & 0.088 & 0.126 & 1.581 & 0.079 & 0.106 & 1.465 \\
2 & 0.114 & 0.154 & 0.834 & 0.114 & 0.152 & 0.969 \\
3 & 0.200 & 0.  
\(sint_{1}\cdots sint_{N_{RE}}\) corresponding to \(BLER\) are plotted in Figure 7. When wide-band signal passed through a fading channel, each RE suffered different fading. Thus, the results show that \(N_{RE}\)\(sinr_{k}\)s spread widely. AWGN indicates SNR and BLERs at CQI 1, 5, 10 and 15 in 5G NR AWGN, as shown in Figure 5. To perform EESM L2S mapping, we applied optimal parameters of \(\alpha_{1}^{*}\) and \(\alpha_{2}^{*}\) obtained using RMSProp optimizer, as shown in Table 6. Blue x mark presents effective SNRs and the results show that widespread \(sinr_{1}\cdots sinr_{N_{RE}}\) were mapped to one effective SNR on the AWGN curve. Figure 8 shows how well is the effective SNR mapped to the AWGN for all CQIs. The results show that most blue x marks were on the AWGN line. With these results, we observed that the proposed method predicted the BLER quite well.  
Figures 9 and 10 show the results of effective SNR mapping by the proposed ML-based EESM method in the case of 5G TDL-A 2 \(\times\) 2 MIMO. MIMO cases were also similar to SISO cases from the analytical point of view. As shown in Figure 9, \(sinr_{1}\cdots sinr_{N_{RE}}\) were more widespread compared to SISO case shown in Figure 9. Therefore, since the variance of \(sinr_{1}\cdots sinr_{N_{RE}}\) was larger than that of SISO, MSE of MIMO was also larger than that of SISO. Figure 10 shows how well is the effective SNR mapped to the AWGN for all CQIs under 2 \(\times\) 2 MIMO. Compared to SISO results, there were a few blue x that were slightly off the AWGN line.  
Figure 8: Effective SNR mapping results of the proposed ML-based EESM method under 5G CDL-A SISO for all CQIs.  
Figure 7: Effective SNR mapping results of the proposed ML-based EESM method under 5G CDL-A SISO (for CQI1, CQI5, CQI10, and CQI15).