## 3 Materials and Methods
### Proposed Deep Learning Model  
This section proposes and explains the proposed deep learning model. Prior to inputting the variables into the recommended deep learning model, a normalization step is required to make the learning stage of the model weights easier. The variables are all normalized before being entered into the model using the min-max scaling strategy. The last dense layer's output parameters \(d_{RX}\), \(EE\), and \(R\) are calculated using the five input variables \(d_{AX}\), SINRth, \(P_{A}\), \(P_{C}\), \(P_{D}\), and \(P_{V}\). As shown in Figure 4, the model is made up of three distinct phases: 1D-CNN, flattening, and thick layers. Three 1D-CNN layers are  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(N\) & \(-174\) dBm/Hz [4; 33] \\ \(B\) & \(10\) Mbit/s [4; 34] \\ \(\alpha\) & \(4\) \\ \(P_{A}\) & \(23\) dBm [4; 35] \\ \(P_{C}\) & \(23\) dBm [4; 35] \\ \(P_{D}\) & \(23\) dBm [4; 35] \\ \(P_{V}\) & \(23\) dBm [4; 35] \\ \(SINR_{th}\) & \(20\) dB [4] \\ Path loss between AV and everything & \((128.1+37.6~{}log_{2}(d_{AX}/km))\) \\ Path loss between CUE and BS & \((148+40~{}log_{2}(d_{CB}/km))\) \\ Path loss between D2D links & \((128.1+37.6~{}log_{2}(d_{DD}/km))\) \\ Path loss between V2V links & \((128.1+37.6~{}log_{2}(d_{VV}/km))\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: System simulation parameters.  
Figure 3: Pearson correlation coefficients of each input parameter (\(d_{AX}\), \(SINR_{th}\), \(P_{A}\), \(P_{C}\), \(P_{D}\), and \(P_{V}\)) and the output (\(d_{RX}\), \(EE\), and \(R\)).  
employed to process the normalized input parameters, each with 128 filters and a kernel of size 1.  
Each 1D-CNN layer generates padded results in order to maintain the width of the output matrix consistently. The output of the third 1D-CNN is then delivered into a flattening layer, which reformats the dimension and prepares it for feeding into the dense  
Figure 4: Proposed deep learning model.  
layers. Following the flattening layer are six dense layers that yield the regression result. Before settling on the number of filters for the 1D-CNN and the number of nodes for the dense layers, a grid search was performed to evaluate numerous combinations. All hidden layers were activated using the Rectified Linear Unit (ReLU). The grid search also included experimenting with various strategies that might follow the hidden layers in the suggested model while taking activation function selection into account. The output of each hidden layer was fed into the best-performing parametric rectified linear unit (PReLU) activation function. The optimization used in the proposed model with the mean absolute error (MAE) loss function and root mean squared error (RMSE) as targets is the adaptive moment (Adam), which may adaptively learn the appropriate parameters based on the learning process. MAE is responsible for assessing the average difference between real and predicted values, whereas RMSE is the root square of the average of the squared differences between actual and predicted values, as represented by  
\[MAE=\frac{\sum_{j=1}^{n}\left|y_{j}-x_{j}\right|}{n} \tag{16}\]  
\[RMSE=\frac{\sum_{j=1}^{n}\left(y_{j}-x_{j}\right)^{2}}{n} \tag{17}\]  
where n is the total number of recorded data, \(y_{j}\) denotes the actual value, and \(x_{j}\) denotes the predicted value. The experiments utilized to train, test, and evaluate the proposed model are described in the next section.