## 3 MIMO System Model and the Implementation Steps of Channel Estimation
### Statistical Report on the Implemented Channel Estimation Model  
The statistical report on the newly initiated channel estimation model for the MIMO system with the aid of the hybrid serial cascaded network in a wireless network is given in Table 5. The mean value of the initiated RP-WHEVO-based channel estimation model is 2.53% more enhanced than EBOA-HSCN, 7.68% more enhanced than GMO-HSCN, 8.42% more enhanced than COA-HSCN, and 2.31% more enhanced than AVOA-HSCN for the SNR rate 5. From this statistical report, it is proved that the ability of the RP-WHEVO-based channel estimation model in the MIMO system using SNR information at the output side of the antenna is upgraded, and the BER is reduced effectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Performance Measures** & **SCMA + LSTM** & **LSTM** & **SCMA** & **DNN** & **RP-WHEVO-HSCN** \\ \hline MEP & 30.445 & 30.005 & 29.583 & 29.176 & 27.2 \\ SMAPE & 46.406 & 45.505 & 44.658 & 43.842 & 39.994 \\ MASE & 12.59 & 12.152 & 11.793 & 11.43 & 97.987 \\ MAE & 30.445 & 30.005 & 29.583 & 29.176 & 0.272 \\ RMSE & 45.182 & 44.625 & 44.103 & 43.592 & 41.121 \\ L1-NORM & 76.112 & 75.013 & 73.956 & 72.94 & 679.99 \\ L2-NORM & 22.591 & 22.313 & 22.052 & 21.796 & 20.561 \\ L-INF-NORM & 96.392 & 96.392 & 96.128 & 95.776 & 95.307 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance Analysis on the Developed Serial Cascaded Deep Learning-Based Channel Estimation Model against Various Techniques.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metrics/Algorithm** & **AVOA** & **COA** & **GMO** & **EBOA** & **RP-WHEVO** \\ \hline \multicolumn{5}{c}{SNR 5} \\ BEST & 0.022696 & 0.023928 & 0.018772 & 0.019241 & 0.019241 \\ MEAN & 0.023363 & 0.024313 & 0.01904 & 0.019791 & 0.019791 \\ WORST & 0.026414 & 0.0262 & 0.020967 & 0.022881 & 0.022881 \\ Standard Deviation & 0.001231 & 0.000742 & 0.00055 & 0.001036 & 0.001036 \\ MEDIAN & 0.022696 & 0.023928 & 0.018772 & 0.019241 & 0.019241 \\  & \multicolumn{5}{c}{SNR 10} \\ Standard Deviation & 0.003382 & 0.004092 & 0.002133 & 0.00139 & 0.00139 \\ WORST & 0.039454 & 0.043228 & 0.028812 & 0.026738 & 0.026738 \\ BEST & 0.022696 & 0.023928 & 0.018772 & 0.019241 & 0.019241 \\ MEDIAN & 0.024074 & 0.02402 & 0.018237 & 0.019991 & 0.019991 \\ MEAN & 0.025101 & 0.025242 & 0.018846 & 0.020405 & 0.020405 \\  & \multicolumn{5}{c}{SNR 15} \\ MEAN & 0.040393 & 0.041285 & 0.027178 & 0.027986 & 0.027986 \\ Standard Deviation & 0.001089 & 0.003061 & 0.003061 & 0.001089 & 0.001089 \\ MEDIAN & 0.039776 & 0.039528 & 0.025421 & 0.027369 & 0.027369 \\ WORST & 0.0431 & 0.047179 & 0.033072 & 0.030693 & 0.030693 \\ BEST & 0.039776 & 0.039528 & 0.025421 & 0.027369 & 0.027369 \\  & \multicolumn{5}{c}{SNR 20} \\ Standard Deviation & 0.00158 & 0.002406 & 0.002249 & 0.001379 & 0.001379 \\ WORST & 0.032975 & 0.039354 & 0.032428 & 0.027749 & 0.027749 \\ MEDIAN & 0.024155 & 0.02755 & 0.022416 & 0.020721 & 0.020721 \\ MEAN & 0.024973 & 0.029315 & 0.024137 & 0.021495 & 0.021495 \\ BEST & 0.024155 & 0.02755 & 0.022416 & 0.020721 & 0.020721 \\  & \multicolumn{5}{c}{SNR 25} \\ BEST & 0.021298 & 0.021358 & 0.016389 & 0.018029 & 0.018029 \\ MEDIAN & 0.021298 & 0.021358 & 0.016389 & 0.018029 & 0.018029 \\ MEAN & 0.021833 & 0.022071 & 0.016992 & 0.018454 & 0.018454 \\ WORST & 0.03049 & 0.029783 & 0.024584 & 0.026991 & 0.026991 \\ Standard Deviation & 0.001467 & 0.001796 & 0.001627 & 0.001323 & 0.001323 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistical Evaluation of the Developed Deep Learning-Based Channel Estimation against Various Algorithms.