## 2 Material and Methods
### Simulation of Analytical Model  
The proposed analytical problem illustrated in Section 2.1, was simulated using MATLAB to visualize the effect of changing different influencing parameters and generating a dataset that is to be used to train a deep learning model. The simulation parameters applied are illustrated in Table 3. Different ranges of values were applied to the equations presented in Section 2.1, thus resulting in a dataset of 33,800 records. Each record represents an example of different combinations of the distance between V and X (\(d_{V2X}\)), the distance between the interfering devices I and X (\(d_{I2X}\)), vehicle transmission power (\(P_{V}\)), the required signal-to-interference-plus-noise-ratio threshold (\(SINR_{th}\)), the required quality of service (\(U\)), and the resulting interference transmission power (\(P_{I}\)).  
Table 4 shows the statistics calculated from the generated dataset for each input and the output \(P_{I}\). The table shows the mean, standard deviation, minimum and maximum value of each parameter. The table shows that the range of the output parameter \(P_{I}\) varies between \(-16.1\) and \(46.84\) dBm.  
Figure 2 displays the Pearson coefficients showing the correlation between all input and output parameters. The figure shows that the output \(P_{I}\) has a high negative correlation with the \(U\) parameter and a high positive correlation with the \(P_{V}\) parameter yet, a low  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(d_{V2X}\) & \(d_{I2X}\) & \(P_{V}\) & \(SINR_{th}\) & \(U\) & \(P_{I}\) \\ \hline Number of & & & & & & \\ records & 33,800 & 33,800 & 33,800 & 33,800 & 33,800 \\ \hline Mean & 61.237 & 122.473 & 20.567 & 10 & 0.702 & 16.944 \\ \hline Standard & & & & & & \\ Deviation & 31.237 & 62.475 & 5.675 & 4.528 & 0.296 & 14.783 \\ \hline Minimum & 1 & 2 & 0 & 0 & 0.1 & \(-16.1\) \\ \hline Maximum & 150 & 300 & 23 & 20 & 0.999 & 46.84 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical analysis of the generated dataset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline \(No\) & \(-174\) dBm [15] \\ \hline \(B\) & 10 MHz [17] \\ \hline \(P_{Vmax}\) & 23 dBm [17] \\ \hline Size of packet & 512 bytes \\ \hline \(f_{c}\) & 5.9 GHz \\ \hline Time for the simulation & 1000 Sec \\ \hline Vehicles count & 100\(\sim\)250 \\ \hline Velocity & 30\(\sim\)60 km/h \\ \hline Traffic Agent & UDP \\ \hline MAC Protocol & IEEE 802.11p \\ \hline Queue & PriQueue with size of 50 Packets \\ \hline Propagation mode & Two Ray Model \\ \hline Antenna & Omni-directional with a height of 1 m \\ \hline Routing Protocol & DSRC \\ \hline Seed count & 10 \\ \hline Type of Noise & AWGN \\ \hline \(PL_{V2V}\) & \(128.1+37.6\log_{10}\) (\(d_{V2V}\)) [37] \\ \hline \(PL_{V2I}\) & \(128.1+37.6\log_{10}\) (\(d_{V2I}\)) [37] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Network simulation parameters.  
correlation with \(d_{V2X}\), \(d_{I2X}\), and \(SINR_{th}\) parameters. Each of these parameters will have an influence that is to be learned by the deep learning model. The figure also shows that parameters \(U\) and \(P_{V}\) have a medium correlation to one another, thus providing some level of redundancy. Since the deep learning model attempts to resemble the human nervous system, having redundancy as a biological fundamental, it is acceptable to have partial redundancy within a deep learning model [38]. Furthermore, the authors in [39] proved that such redundancy tends to enhance the performance of neural-based models.