## 4 Simulation and Results
### Testing ML Algorithm  
In the next step, we estimated two performance parameters of the proposed approach: the probability of power loss at the receiver by more than 3 dB compared to the maximum achievable effective isotropic radiation power (EIRP), and the average deviation of the received signal level from the target for a range of traffic intensity values. In this regard, to evaluate the algorithm's robustness to the non-standard behavior of drivers, such as increased reaction time to a traffic situation or driving in excess of the speed limit, the deviation was introduced into the traffic parameters for a certain number of vehicles at random time moments. A variety of scenarios of traffic situations were created by regulating the intensity of traffic and the probability of atypical vehicle behavior. In future work, the range of factors considered can be expanded, for example, by weather conditions that affect the coefficient of vehicle adhesion to the road. Table 3 shows the simulation results of the algorithm performance when beamsteering the PAA located at the V2I base station for an hour of traffic dynamics in the service area. It can be seen that the average value of the signal's power deviations at the receiver input is always different from zero. This is due to the beam moving discretely between sub-sectors and uneven gain within the sub-sector provided by the shape of RP. At the fringe area between sub-sectors, the signal power decreases by 3 dB, which should be particularly considered for low traffic intensity when a vehicle moving at high speed enters  
Figure 5: Error matrix of ML algorithm operation on test data. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Traffic Intensity, Car Per** & **Vehicles with Atypical** & **Average Deviation of** & **Probability of Power** \\
**Hour** & **Behavior, \%** & **Received Signal Level,** & **Reduction by More than** \\  & & **dB** & **3 dB** \\ \hline
1222.95 & 0 & \(-\)2.43 & 0.06 \\
1227.17 & 10 & \(-\)2.67 & 0.07 \\
1219.37 & 20 & \(-\)2.85 & 0.08 \\
1218.34 & 30 & \(-\)2.76 & 0.084 \\
1220.13 & 40 & \(-\)3.01 & 0.095 \\
2552.52 & 0 & \(-\)2.14 & 0.04 \\
2769.1 & 10 & \(-\)2.81 & 0.07 \\
2840.12 & 20 & \(-\)3.11 & 0.071 \\
3256.17 & 0 & \(-\)2.05 & 0.05 \\
3535.34 & 10 & \(-\)3.21 & 0.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation results of the algorithm performance. the zone of low-quality service. However, when traffic is high, the atypical behavior of the vehicle has a more significant impact since the nature of its movement between the request message and the initialization of the connection can change significantly (for example, sudden acceleration, braking, or turning). In turn, the sudden acceleration in the case of low traffic intensity is more correlated with the data from the request message, leading to more minor errors in determining the vehicle's location at the next moment in time. One way to level out this factor at high traffic intensity is to include examples of atypical vehicle behavior in the ML's training set. The results showed a decrease in the average signal power at the vehicle's receiver compared to [27] due to the above factors. However, at the same time, learning the ML algorithm according to the proposed approach makes it more relevant to natural conditions, especially in the case of road sections with high traffic intensity. Another issue in the performed simulation is the applied coefficient of the car's adhesion to the road, which was set to the case of dry asphalt. Hence, the application of the proposed algorithm is limited by the weather conditions. Deviation in the weather for a model can increase the frequency of non-standard vehicle behavior. It is possible to eliminate this limitation by training the ML algorithm on datasets under different weather conditions, obtaining several decision trees, and including a weather station [57, 58] in the V2I base station to determine the switching moment and the type of trained tree.