## 4 Simulation Results
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Max epochs & 100 \\ Learning rate & 0.01 \\ Dilation factors & 1, 2, 4, 8 \\ Number of filters & 64 \\ Dropout factor & 50\% \\ Input features & 8 \\ Number of output class & 16 \\ Kernel size & \(5\times 5\) \\ Optimizer & Adam, SGDm, RMSprop \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model training parameters. Figure 5: Training progress of proposed TCN model: (**a**) training accuracy with 50 epochs, (**b**) validation accuracy with 50 epochs, (**c**) validation accuracy with 50 epochs, and (**d**) validation loss with 50 epochs. investigation is needed for the performance improvement. In the future, the TCN model will be tuned with proper parameters for obtaining a more optimal performance. Figure 6 shows the BER and SER performance of the proposed TCN model with consideration of the QPSK modulation. Figure 6a,b show the performance comparison with the proposed TCN, LSTM model, and without TCN model in terms of the BER and SER results. The results are performed by comparison of the three optimizers, Adam, SGDm, and RMSprop. From Figure 6a, it is shown that the performance of the BER with the Adam optimizer achieves better results compared to the two others. Next, the RMSprop optimizer as compared to the SGDm optimizer achieves a good BER. The overall error trend of the SER follows the BER trend which is shown in Figure 6b. It is shown that the Adam optimizer provides a better SER performance compared to RMSprop and SGDm. Again, it is shown from Figure 6b that RMSprop has a better performance than SDGm. From Figures 6 and 7, it is observed that the proposed TCN model outperforms the LSTM and without TCN model in terms of the SER and BER simulation results with different optimization algorithms. In addition, it is noticeable that, with SGDm optimization, the LSTM model performance is very poor compared to the proposed TCN with the same simulation parameters. On the other hand, Figure 7 represents the performance of the BER and SER of the proposed TCN model with consideration of the BPSK modulation. Figure 7a,b show the performance comparison with the proposed TCN, LSTM model, and without TCN model in terms of the BER and SER results. In this case, it is shown from Figure 7a that SGDm has a better performance as compared to the Adam and RMSprop optimizers. When fewer constellation points in the modulation scheme are used, SGDm can decode the received bits successfully. After that, Adam has the second highest BER performance and SGDm gives the worst performance in the case of the BER. Figure 7b shows the SER performance of the three optimizers, and a similar trend is shown like the BER results. In addition, compared with the LSTM and without TCN method, the proposed TCN-based model provides a better performance in terms of different SNR values. Figure 6: (**a**) The BER results with respect to SNR of different optimizers used for TCN model using QPSK modulation, (**b**) represents SER performance with respect to SNR values for the QPSK modulation scheme for different optimizers. Figure 8a shows the BER result for the 2 \(\times\) 2 MIMO with 256 and 512 RIS elements and the 4 \(\times\) 4 MIMO with 256 and 512 RIS elements. For the 2 \(\times\) 2 MIMO, the BER performance is improved according to the number of RIS elements. Again, for the 4 \(\times\) 4 increasing RIS elements number, the BER performance has no significant changes for different SNRs. Figure 8b shows the SER performance for the 2 \(\times\) 2 MIMO with 256 and 512 RIS elements and 4 \(\times\) 4 MIMO with 256 and 512 RIS elements. For the 2 \(\times\) 2 increasing RIS elements number, the SER performance is improved. However, for the 4 \(\times\) 4 MIMO, the SER performance has no significant changes for different SNRs according to the number of RIS elements.