## 4 Results and Discussion
The results obtained show the effectiveness of the proposed model as it proves its ability to adapt itself under different circumstances and environmental conditions to face the challenges of achieving the required system performance and sending reliable data to any destination. When evaluating the system's performance for the same previously assumed scenario, assuming high interference power where all interfere devices send data with high transmission power (23 dBm), the same result is reached in terms of achievable data rate (\(R\)), as shown in Figure 8. Figure 8a shows that when \(\mathit{SINR}_{th}\) is 0 dB and the system is subjected to significant interference, the suggested model works effectively when \(P_{A}\) exceeds 8 dBm for both the analytical and deep learning models. Furthermore, when \(\mathit{SINR}_{th}\) is 20 dB, as shown in Figure 8b, the system outperforms the model given in [4] when \(P_{A}\) exceeds 8 dBm for both the analytical and deep learning models. As discussed previously, boosting \(P_{A}\) raises the overall system achievable data rate since it helps overcome interference and improve system performance. The results obtained in Figure 8 show the capacity of the proposed model to learn and adapt to complex interference settings, capture non-linear correlations, and efficiently exploit available data for interference mitigation. These benefits  
Figure 6: Required \(\mathit{SINR}_{th}\) (\(d_{IR}\)) vs. overall system achievable data rate (\(R\)) [4]. enable the model to outperform standard analytical models, particularly in circumstances involving heavy interference and variable \(\mathit{SINR}_{\mathit{th}}\). Figure 8: Autonomous vehicle transmission power (\(P_{A}\)) and overall system achievable data (\(R\)) (high interference). (**a**) Low required \(\mathrm{SINR}_{\mathit{th}}\) (**b**) High required \(\mathrm{SINR}_{\mathit{th}}\) (4). Figure 7: Autonomous vehicle transmission power (\(P_{A}\)) and overall system achievable data (\(R\)) (low interference). (**a**) Low required \(\mathit{SINR}_{\mathit{th}}\) (**b**) High required \(\mathit{SINR}_{\mathit{th}}\) (4). For further evaluation, we have compared the performance of the proposed approach during high and low interference for two different \(SINR_{th}\), which are 0 dB and 20 dB, as illustrated in Figure 9. Figure 9a demonstrates the system's achievable data rate when the system faces low interference; thus, it can be found that the achievable data rate increases when \(P_{A}\) increases for both assumed \(SINR_{th}\). Additionally, it is worth mentioning that the proposed approach performs well when \(SINR_{th}\) is 20 dB compared with the performance when \(SINR_{th}\) is 0 dB. The same performance is obtained when the system faces high interference, as depicted in Figure 9b; it is noted that the achievable data rate increases when \(P_{A}\) increases for both assumed \(SINR_{th}\), and the system performs well when \(SINR_{th}\) is 20 dB compared with 0 dB for both the analytical and deep learning models. From the results obtained, it should be mentioned that while increasing the needed \(SINR\) can possibly result in higher achievable data rates, there is a practical limit. At some point, a careful balance must be struck between increasing transmission power to achieve higher \(SINR\) requirements and the risk of increased interference from neighboring devices or other environmental conditions. Moreover, raising the required \(SINR\) threshold necessitates a stronger and more robust sent signal, resulting in greater interference mitigation, decreased errors, increased channel capacity, and more efficient modulation and coding. All of these elements contribute to a higher achievable data rate, which shows the significance of the proposed approach.