## 4 Results and Discussion
Furthermore, the system's performance has been evaluated in terms of \(EE\) vs. the required \(SINR_{th}\) when the system faces low and high interference, as shown in Figure 10. Figure 10 shows the proposed approach can overcome interference whether low or high and reach the optimum \(EE\) for each \(SINR_{th}\). Also, it can be noted that increasing the required \(SINR_{th}\) leads to increasing the overall system \(EE\) for both analytical and deep learning models. Higher \(EE\) shows that the system is making better use of its energy resources to meet communication goals. Additionally, increasing the needed \(SINR_{th}\) improves energy efficiency due to system modifications such as transmission power optimization, enhanced  
Figure 9: Autonomous vehicle transmission power (\(P_{A}\)) and overall system achievable data (R). (**a**) Comparison between low and high \(SINR_{th}\) in case of low interference (**b**) Comparison between low and high \(SINR_{th}\) in case of high interference. signal quality, adaptive modulation and coding, and dynamic resource allocation. These changes contribute to conveying data more effectively per unit of energy spent, resulting in the observed rise in overall system \(EE\) for both the analytical and deep learning models. For further investigation, the system is evaluated in terms of \(EE\) and versus variable AV transmission power (\(P_{A}\)), as well as when the system encounters high and low interference for two distinct \(SINR_{th}\) values of 0 dB and 20 dB, as shown in Figure 11. Figure 11a shows the system \(EE\) when the system is subjected to low interference; consequently, \(EE\) increases as \(P_{A}\) increases for both assumed \(SINR_{th}\). Furthermore, when \(\text{SINR}_{th}\) is 20 dB, the suggested approach performs well in terms of \(EE\) when compared to the performance when \(SINR_{th}\) is 0 dB. When the system is subjected to severe interference, as shown in Figure 11b, it is observed that \(EE\) increases with the increment of \(P_{A}\) for both assumed \(SINR_{th}\), and the system works well when \(SINR_{th}\) is 20 dB compared to 0 dB for both the analytical and deep learning models. Thus, it is worth mentioning that increasing transmission power enhances the transmitted signal, boosting \(SINR\) and resulting in improved \(EE\) by delivering more data per unit of energy. Also, increasing the required \(SINR_{th}\) needs a stronger signal, resulting in increased energy efficiency by focusing energy on sending a reliable signal even in the presence of interference and noise. To emphasize the significance of the suggested model, a comparison with one of the most current research papers [28] was conducted, as shown in Figure 7.0 The findings shown in Figure 12 clearly show that our proposed approach outperforms the model introduced in [28] in terms of achievable data rate. This superiority can be due to the adaptive nature of our proposed model, which allows it to dynamically respond to channel characteristics such as transmission distance, path loss, and interference power. The proposed model consistently achieves the desired system performance by picking the most appropriate transmission scheme based on these conditions. Additionally, it should be noted that when the transmission power reaches 30 dBm, the model proposed in [28] outperforms our proposed model. However, this is noteworthy as our proposed model does not aim to reach such high transmission power values. Instead, it adapts itself based on channel conditions to select the suitable power level that fulfills the system requirements, ultimately maximizing the system's energy efficiency. Figure 7.1 Required \(SINR_{th}\) (\(d_{IR}\)) vs. overall system energy efficiency (\(EE\)). Figure 7.2 Required \(SINR_{th}\) (\(d_{IR}\)) vs. overall system energy efficiency (\(EE\)). (**a**) \(EE\) performance with low and high \(\text{SINR}_{th}\) in case of low interference (**b**) \(EE\) performance with low and high \(\text{SINR}_{th}\) in case of high interference.