## 4 Results and Discussion
### Simulation Scenario and Parameters  
The general simulation scenario is presented in Figure 4. The sections in the white represent the roads in the simulation scenario, and the sections in blue represent the buildings. The car in Figure 4 represents a possible position for the vehicle in the simulation scenario. Because of the symmetries present in the simulation scenarios, obtaining results from the left side of the horizontal lane was, on average, the same as obtaining results from every position in the simulation scenario. Taking into account this particularity, all the positions for the cars were considered on the left side of the horizontal road. Furthermore, as the work focused on increasing the communication range for urban V2V communications, the evaluation of the model was done by only considering a single  
Figure 4: Simulation scenario. transmitting and receiving vehicle. This simplification had the downside of not taking vehicular density into account. The reason vehicular density was not taken into account was that the work focused on comparing different communication schemes in the same simulation scenario. While taking the vehicular density into account would make the results more accurate, it would affect the communication schemes similarly, and thus, the comparison would be similar. Besides, the complexity of a simulation setup that considers the impact of the vehicular density is much greater, and consequently, the interpretation of the results would also be, so this analysis was outside the scope of this research work. Nevertheless, the effect of vehicular density in the resulting performance is an interesting point to consider, and thus, it is proposed as future work. To consider this effect, the simulation scenario should be extended to consider multiple vehicles communicating at the same time, and consider the link layer protocols specified in the IEEE 802.11p standard [15]. Interestingly, considering vehicular density might benefit learned beam shapes even further, since models could learn to better utilize the channel by separating it spatially. As mentioned previously, a channel model for urban V2V communications presented in [31] was used for our observations. This channel model had a number of different parameters that related to the shape of the intersection and were used in Equation (1). Note that the simulation scenario presented in Figure 4 was similar to the X-junction presented in [31]. Consequently, the following parameters were adopted in the work. The value for the road width presented in Table 1 was calculated by considering the value for the lane width of 4 m presented in [34]. In this work, a road with two lanes and side walks was considered. The side walks had a width of 1 m, which resulted in a road width of 10 m.  
As previously noted, this work considered the IEEE 802.11p standard [15] for the physical layer of the vehicular communication link. Table 2 summarizes the key parameters considered for the communication link. The Rx sensitivity had a slightly higher value than the one presented in [31], which was 23 dBm. This was because the value presented in [31] considered a packet error rate of 10%, which was too high for safety applications in V2V communications. Finally, the algorithms had different hyper-parameters that had to be tuned for successful training, which are illustrated in Table 3. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(g_{1}\) & 0.12 [31] \\ \(g_{2}\) & 0.58 [31] \\ \(m\) & 4.18 dB [31] \\ \(\sigma\) & 4.28 dB [31] \\ Road width & 10 m [34] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters for the simulation scenario. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Frequency & 5.9 GHz [15] \\ Data rate & 6 Mbps [15] \\ Beacon rate & 10 beacon/s [15] \\ Tx output power & 20 dBm [31] \\ Rx sensitivity & \(-67\) dBm [31] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters for the communication link.