# Modeling and Performance Analysis of mmWave and WiFi-Based Vehicle Communications
## 4 Simulation Results  
This section is dedicated to presenting the different simulation results and evaluating the performance of the proposed system model in the cases of mmwave and WiFi using the R2020a version of MatLab tool. Without loss of generality, the used simulation parameters are set to be: \(N=150\), \(d_{min}=5\) m, \(\lambda_{ V}=\left[1e-22e-23e-2\right]\) m\({}^{-1}\), \(f_{\text{mmWave}}=5\) GHz, \(f_{\text{mmWave}}=30,50,70\) GHz, \(Bw_{\text{Wifi}}=2\) MHz, \(B_{\text{mmWave}}=100\) MHz, \(\mathcal{P}_{ T}=10\) dB, \(\mathcal{P}_{ N}=-100\) dB, \(\gamma_{\text{tr}}=2\), \(\alpha_{1}=2.2\), \(\alpha_{2}=3\), \(\beta_{1}=2\), \(\beta_{2}=2.8\), \(\mu_{\text{In}(S_{1})}=-2\), \(\mu_{\text{In}(S_{2})}=-4\), \(\sigma_{\ln(S_{1})}^{2}=1\)\(\sigma_{\ln(S_{2})}^{2}=2\), \(\Omega_{1}=1\), \(\Omega_{2}=0.8\), \(m_{1}=1\), \(m_{2}=2\), \(\mu_{1}=1\), \(\mu_{2}=0.9\), \(\mathcal{K}=0.9\). They are displayed in Table 4.  
Figure 3 illustrates the PLoS variations versus the transmission distance between vehicles, incorporating various vehicle density values within two modeling scenarios, e.g., 2D and 3D. As shown in this figure, the LoS probability increases with the decreased distance between vehicles. This is due to the fact that decreasing the transmission distance decreases the number of vehicles between the transmitter and the receiver, and hence the LoS probability increases. In addition, it is shown that the larger the density of obstacle vehicles is, the lower the PLoS is, which is expected. This is because the probability of the line of sight decreases in a high-traffic environment in both 2D and 3D modeling scenarios. Figure 4 illustrates a comparison between the performance of WiFi and millimeter waves in an LoS scenario, depicting the evolution of the relevant STP versus the transmission distance, in a 3D modeling scenario. The results clearly demonstrate that as the distance between vehicles increases, the STP value decreases. Notably, the STP for WiFi remains nearly constant and does not decrease. In contrast, millimeter waves exhibit a significant decrease in STP as the distance between vehicles increases. Moreover, for millimeter waves, the STP decreases with the increase in frequency bandwidth. Thus, the main interpretation of these results lies in the observation that millimeter waves present poor \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(N\) & \(150\) \\ \hline \(d_{min}\) & \(5\) \\ \hline \(\lambda_{V}\) & \([1e-22e-23e-2]\) \\ \hline \(f_{\text{\tiny{NIFI}}}\) & \(5\,\text{GHz}\) \\ \hline \(f_{\text{\tiny{emNovo}}}\) & \(30,50,70\,\text{GHz}\) \\ \hline \(B_{\text{\tiny{NIFI}}}\) & \(2\,\text{MHz}\) \\ \hline \(B_{\text{\tiny{emNovo}}}\) & \(100\,\text{MHz}\) \\ \hline \(\mathcal{P}_{T}\) & \(10\,\text{[dB]}\) \\ \hline \(\mathcal{P}_{N}\) & \(-100\,\text{[dB]}\) \\ \hline \(\gamma_{th}\) & \(2\) \\ \hline \(a_{1}\) & \(2.2\) \\ \hline \(a_{2}\) & \(3\) \\ \hline \(\beta_{1}\) & \(2\) \\ \hline \(\beta_{2}\) & \(2.8\) \\ \hline \(C\) & \(3e8\) \\ \hline \(\mu_{\ln(S_{1})}\) & \(-2\) \\ \hline \(\mu_{\ln(S_{2})}\) & \(-4\) \\ \hline \(\sigma_{\ln(S_{1})}^{2}\) & \(1\) \\ \hline \(\sigma_{\ln(S_{2})}^{2}\) & \(2\) \\ \hline \(\Omega_{1}\) & \(1\) \\ \hline \(\Omega_{2}\) & \(0.8\) \\ \hline \(m_{1}\) & \(1\) \\ \hline \(m_{2}\) & \(2\) \\ \hline \(\mu_{1}\) & \(1\) \\ \hline \(\mu_{2}\) & \(0.9\) \\ \hline \(\mathcal{K}\) & \(0.9\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The values of the simulation parameters. cles increases, both millimeter waves and WiFi experience a decrease in average throughput. However, mmWaves exhibit superior AT compared to WiFi, especially with a larger frequency bandwidth. Therefore, the mmWaves demonstrate notably higher throughput than WiFi in an LoS scenario, explained by the advantage of mmWaves that use a wider frequency band compared to WiFi.