## 3 Simulation Results
### Performance Evaluation
Figure 10 shows the comparison of the proposed CNN-GRU decoding performance with the traditional demodulation system, Demod-CNN model, and LSTM model according to the SER versus different SNR values. The graph shows that the proposed CNN-GRU-based demodulation system outperforms all other methods. Evidently, the proposed model vastly outperforms other systems at SNR ranging from 0 to 20 dB and SER reaches approximately \(10^{-4}\). Contrarily, the conventional method and the Demod-CNN model have SNR ranges of 0 to 30 dB and SER of approximately \(10^{-3}\) and \(10^{-4}\), respectively. Unlike the BER accuracy of the proposed CNN-GRU model, SER performance is also higher than the conventional, Demod-CNN, TCN, and LSTM models and shows significant improvement in decoding capability. However, due to the lower SNR, the LSTM model shows lower performance than the conventional and Demod-CNN models, but with higher SNR, it follows the proposed model performance. Additionally, the TCN model has the same performance with SNR range (0-6) dB, and after that, it follows the LSTM. It is seen from the above simulation outputs that the CNN-GRU gains a noticeable performance difference from the other four schemes. The summary is from the above simulation results; in order to enhance BER and SER for RIS-based systems, the proposed model can be an effective solution. Figure 11 shows the analysis of SER and BER of the proposed CNN-GRU model by changing different model parameters during the training of the model. The changing parameters are considered three well-known optimization algorithms, namely, Adam, SGDm, and RMSprop. In addition, to conform to the model learning rate (LR), we have configured the training parameters by considering the LR of 0.01 and 0.001, respectively. Figure 11a,b indicate that the simulation results are taken under the LR of 0.01 and three optimization algorithms. In Figure 11a, with the same LR, the Adam optimizer shows better SER performance than the others with different SNR values, and among SDGm and RMSprop, RMSprop achieves a better SER performance than SGDm. A similar performance happens for BER of Figure 11b. On the other hand, the simulation results of SER and BER with LR of 0.001 and three optimizers are shown in Figure 11c,d. Figure 11c,d indicate that with LR of 0.001, the SER and BER performance of the proposed CNN-GRU model is less than LR 0.01. Furthermore, the Adam optimizer shows a better performance compared to the others. It is indicated that in the lower SNR part, the three optimization algorithms show similar performance and follow the same trend, which confirms the proposed model robustness with three optimization algorithms. Figure 10: SER simulation results of CNN-GRU-based RIS-assisted MIMO communication. Figure 12 shows the throughput analysis comparison of the proposed CNN-GRU with the traditional method, Demod-CNN model, TCN, and LSTM model regarding different SNR values. The graph demonstrates that the suggested model performs almost as well as other approaches in the SNR regions of (0-5) dB. At the initial values of the SNR, the achievable rate of the CNN-GRU is 1.69 bps, which is similar to the other methods and is 1.85 bps with 5 dB SNR. After 5 dB SNR, the proposed CNN-GRU outperforms the other methods, and it achieves the maximum rate at a 20 dB SNR value. Thus, the proposed model exhibits the robustness of handling optimal prediction rates in different SNR ranges. Figure 11: SER and BER simulation results of CNN-GRU model with different learning rate (LR) and optimization algorithms for RIS-assisted MIMO communication: (**a**) SER with LR = 0.01; (**b**) BER with LR = 0.01; (**c**) SER with LR = 0.001; (**d**) BER with LR = 0.001. Figure 12: Throughput simulation results of CNN-GRU-based RIS-assisted MIMO communication.