## 4 Simulation Results  
In this section, the outcome of the proposed model based on RIS-assisted communication is presented. The simulation results are obtained under the hardware system environment of Windows 10 pro with a graphics processing unit (GPU). The programming is performed with MATLAB software with the help of the Deep Learning ToolboxTM. The proposed TCN model simulation parameters are shown in Table 2. For the simulation, the BS antenna \(M=2\), RIS reflecting elements \(N=32\times 16\), and number of UEs \(K=2\) are considered for the BS-RIS-UE configuration. The pathways between the BS to RIS and RIS to UE are considered to be \(P_{b}=2\) and \(P_{r,k}=2\), respectively. For the current study, we considered a Rician \(K\)-factor of 15 dB and it was assumed that all of the communication network parameters are static, that all the UEs are in the static position, and the distance from the BS to RIS and the RIS to UE is not changed. To generate the label and corresponding dataset for training the proposed TCN network, two modulation schemes, QPSK and BPSK, are considered. In addition, three optimization algorithms (Adam, stochastic gradient descent with momentum (SGDm), and root mean squared propagation (RMSprop)) are employed during the training of the TCN model. A total of 256,000 dataset samples are generated for training the model, where 80% and 20% of the data are divided for the training and validation of the proposed TCN. At the time of dataset generation, a 30 dB SNR is considered. Then, the TCN network is configured with the training parameters to successfully run it for learning the generated data. To minimize the loss function during training, the above-mentioned optimization  
Figure 4: Block diagram of the proposed TCN-based demodulation technique at the receiver. The received input sequence is fed into the TCN network as (\(8\times 1\times 1\)) features and four TCN blocks employ \(Q^{(1)},Q^{(2)},Q^{(3)},Q^{(4)}\); for the dilated convolution layer, we have used four dilation values (1, 2, 4, 8), kernel size 6, and 64 filters; the final output of \(Q^{(4)}\) is connected to a fully connected layer with dimension (\(16\times 64\times 1\)), followed by a softmax layer with dimension (\(16\times 1\)), and a classification layer with dimension (\(16\times 1\)). algorithms are used. For optimal learning, the learning rate of 0.01 is used. The proposed model has completed 100 epochs for satisfactory learning, and at this stage, the validation accuracy achieved is about 99.97%. The training validation summary of the QPSK with 50 epochs taken is shown in Figure 5a-d where the different graphs indicate the training accuracy and loss and validation accuracy and loss. In this work, for receiving, the multiuser is considered. Therefore, to eliminate the inter-user interference (IUI) on the terminal side, the zero-forcing (ZF) precoder [35] is taken into account. The simulation results under the SER and BER performance with respect to different SNR levels are performed to test the efficiency of the proposed TCN model for the RIS-assisted communication system. To test the performance of the proposed TCN, we have compared the BER and SER with the time-series LSTM model as well as the without TCN model. The calculation of the BER and SER at the receiver side is as follows: (1) the error rate of the BER describes the incorrect demodulated bit at the receiver and (2) the incorrect categorization of the received symbol means the error rate for the SER. Thus, in the simulation results, the BER and SER of the BPSK are different for the proposed system. In addition, the simulation results of the BER and SER for the BPSK and QPSK are taken over the AWGN. In this study, the application of the TCN is the pilot project for testing the RIS-based environment and further