# Advanced Optimization Algorithm Combining a Fuzzy Inference System for Vehicular Communications
## 4 Simulation Results  
This study presents a comparative result of the PRR between two configurations across diverse scenarios, as illustrated in Figures 7-10. The results demonstrate that the FIRL method consistently outperforms the alternative configuration in most scenarios examined. Figure 7 shows the results of low-density situations for \(n_{V}=100\). From Figure 7, it is observed that the PRR decreases with the increasing distance. However, an exception is observed in low-density situations, where FIRL's effectiveness is limited. This anomaly can be attributed to the abundance of radio resources available in the network under such conditions, rendering the adaptive mechanisms employed by FIRL ineffective in improving PRR outcomes. These findings highlight the context-dependent nature of FIRL's performance and underscore the importance of considering network density when implementing adaptive radio resource management strategies.  
Figure 8: PRR comparison result for 200 vehicles.  
Figure 7: PRR comparison result for 100 vehicles.  
Figure 9: PRR comparison result for 300 vehicles.  
Figure 7 presents the PRR comparison results for the low-density vehicle scenario. It is observed that while the FIRL algorithm exhibits lower performance at certain vehicle distances, its overall performance surpasses the baseline configuration. In this scenario, the available radio resources can accommodate all vehicle communications simultaneously, suggesting that the current machine learning hyperparameters may not be optimally tuned for all vehicle distances. It is crucial to evaluate the suitability of these hyperparameters to ensure they can deliver the desired performance outcomes across various conditions. Despite the suboptimal performance in the low-density scenario, Figures 8-10 demonstrate the superior performance of the FIRL algorithm and highlight its potential for improving vehicular communication systems.  
Table 3 presents a comprehensive result of PRR improvement percentages for the FIRL system across three different distance scenarios: 50, 100, and 150 m. The data compares FIRL performance against a static baseline for varying values of \(n_{V}\) (ranging from 100 to 400). At 50 m, FIRL demonstrates modest improvements ranging from 1.28% to 19.23%, with the baseline performance gradually declining from 97.58% to 80.89% as \(n_{V}\) increases. The 100 m distance scenario shows more substantial improvements, with FIRL achieving enhancement rates from 5.67% to 67.46%, particularly notable as the baseline performance drops significantly from 91.97% to 53.1%. The most dramatic improvements are observed at 150 m, where FIRL achieves remarkable enhancement rates ranging from 27.91% to 169.83%, compensating for the severe degradation in baseline performance from 72.01% to 28.65%. The table illustrates FIRL's increasing effectiveness at greater distances and higher \(n_{V}\) values, with the most pronounced improvements occurring in challenging transmission conditions.  
In this study, we evaluated the performance of the proposed FIRL approach compared to the baseline configuration, focusing on the impact of vehicle density on network performance. Figure 11 illustrates the PRR as a function of the total number of vehicles, ranging from 100 to 400 in increments of 50, measured at an observation vehicle distance of 50 m. The results demonstrate that PRR performance decreases as vehicle density increases, irrespective of the machine learning technique employed, primarily due to limitations in available radio resources. Notably, the FIRL approach consistently outperforms the baseline configuration, yielding improved PRR outcomes across all tested vehicle densities (\(n_{V}\)). This finding suggests that, under appropriate conditions, a machine learning mechanism can achieve superior PRR performance in scenarios with higher vehicle communication demands, highlighting the potential of FIRL in enhancing vehicular network efficiency.  
Figure 10: PRR comparison result for 400 vehicles.