## 3 Simulation Results
### Performance Evaluation  
Figure 8 shows the prediction performance of the proposed CNN-GRU according to the predicated and tested data where the examined model is learned with the Adam optimizer with learning rate = 0.01 at the time of training. In Figure 8a, the confusion matrix is analyzed in terms of predicted and true classes for the evaluation of prediction accuracy. There are a total of 16 classes for prediction, and it is seen that the classification error of classes is very marginal. Additionally, the performance of the test sequence prediction between predicated and tested data is shown in Figure 8b. In Figure 8b, the orange and blue color-covered regions are presented with test data and predicted data, respectively. To be more clearly visible, the zoom view of the portion of the output is indicated by the blue arrow. It is evident that the miss prediction rate of class with time step is very low and the proposed model is highly efficient. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline The number of RIS elements & 32 \(\times\) 16 \\ BS to RIS paths & 2 \\ RIS to user paths & 4 \\ BS to user paths & 2 \\ Transmit antenna spacing & 0.5 \(\lambda\) \\ RIS elements spacing & 0.5 \(\lambda\) \\ Number of transmitter antenna & 2 \\ User number & 2 \\ BS antenna number & 2 \\ OFDM subcarrier number & 128 \\ Number of cyclic prefixes & 32 \\ Modulation type & QPSK \\ Channel noise & AWGN \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters of the RIS design. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Maximum epochs number & 50 \\ No. of filters in convolutional layer & 64 \\ No. of hidden units in GRU layer & 100 \\ Fully connected layer & 16 \\ Size of minibatch & 100 \\ Learning rate & 0.01 \\ Gradient threshold & 1 \\ Validation frequency & 50 \\ Used optimizer & Adam \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters of the proposed model. Figure 9 depicts the comparison of the proposed CNN-GRU decoding performance with the traditional method, Demod-CNN model, TCN, and LSTM model according to the BER versus different SNR values. The graph absolutely exhibits that the proposed CNN-GRU-based demodulation system outperforms the traditional method, the Demod-CNN model, TCN, and LSTM model. Due to the low SNR value at the SNR (0-5) dB range, the proposed conclusions are less precise but not less than those of the other systems. However, the model performance improves as the SNR values rise up to (5-20) dB. Evidently, the proposed model significantly outperforms other systems at SNR ranging from 0 to 20 dB and BER reaches approximately \(10^{-5}\). In contrast, conventional and Demod-CNN systems cover SNR ranging from 0 to 30 dB and BER reaches around \(10^{-4}\). Up to 5 dB, the BER for the proposed CNN-GRU model follows a similar pattern to all of the methods. After this threshold, the BER for the proposed CNN-GRU model significantly decreases and ends at 20 dB SNR. Additionally, the CNN-GRU is also compared with the LSTM model and TCN model. It can be shown that the suggested model performs better than the LSTM and TCN models with the SNR (0-20) dB range. Because of the low SNR values, the LSTM model performance is degraded more than all other methods, but after 12 dB SNR, its performance follows the proposed CNN-GRU model. In addition, TCN shows a little better performance with (0-3) dB SNR, and after that, the performance is degraded and follows the LSTM trends. This demonstrates how effectively the proposed CNN-GRU model can demodulate and enhance the BER system performance. Figure 8: CNN-GRU-based RIS-assisted MIMO communication prediction performance: (**a**) Confusion matrix of predictions and true class; (**b**) Results of test sequence predictions between test and prediction data (blue arrow indicates the zoom view of a portion of the output for more visible). Figure 9: BER simulation results of CNN-GRU-based RIS-assisted MIMO communication.