## 4 Results and Discussion  
The performance of the proposed analytical and deep learning models is presented in this section. Furthermore, the efficacy of the proposed approach was tested using MATLAB and Python simulations in terms of optimized energy efficiency and achievable data rate. Figure 5 depicts the testing and evaluation of the proposed deep learning model described in Section 3.3. The datasets were divided into two parts: 80% training data and 20% test data. Figure 5a-c indicate the training and validation mean absolute error for the needed \(d_{RX}\), \(EE\), and \(R\), respectively; all of the figures illustrate that there was no need for extra training beyond epoch 100 because the outcomes were barely changing. Furthermore, Figure 5d shows that the independent training and validation error losses decrease and are maintained at a specific point, as well as that the independent training and validation errors for each output were close to the same values, indicating that the proposed model was not overfitted or underfitted. Figure 6 shows the required \(SINR_{th}\) versus the overall achievable data rate for the analytical and deep learning models and compared with the model proposed in [4]. To evaluate the effectiveness of the proposed model, high interference is assumed where all the interfere devices send their data with the highest transmission power. Thus, it can be observed from Figure 6 that the proposed model outperforms the model proposed in [4] in terms of achievable data rate for both the analytical and deep learning models. Also, it can be mentioned for both the analytical and deep learning models that the achievable data rate increases with the increase of \(SINR_{th}\). In the context of V2V communication, obtaining a higher data rate means that vehicles can exchange information more quickly and effectively. This results in faster and more reliable communication between vehicles, allowing them to transmit real-time information such as traffic conditions, road hazards, or changes in driving behaviors. A higher achievable data rate in V2V communication enables more efficient and rapid exchanges between autonomous vehicles, thereby contributing to safer and smoother traffic flow. Figure 5: Training and validation mean absolute error generated during training of the proposed model. (**a**) \(d_{\text{RX}}\) Training and validation mean absolute error vs epochs (**b**) \(EE\) Training and validation mean absolute error vs epochs (**c**) \(R\) Training and validation mean absolute error vs epochs (**d**) Training and validation mean absolute error vs epochs. Furthermore, the system was evaluated in terms of overall system achievable data rate vs. AV transmission power, as depicted in Figure 7. In Figure 7, low interference is assumed where all the interfere devices sent data with low transmission power (17 dBm). The system is evaluated for two different \(\mathit{SINR}_{th}\), as demonstrated in Figure 7a,b. Figure 7a depicts that when \(\mathit{SINR}_{th}\) is 0 dB while the system is facing low interference, the proposed model performs well when \(P_{A}\) exceeds 7 dBm for both the analytical and deep learning models. Additionally, the same result is obtained when \(\mathit{SINR}_{th}\) is 20 dB, as shown in Figure 7b; the system outperforms the model proposed in [4] when \(P_{A}\) exceeds 7 dBm for the analytical and deep learning models. It is worth mentioning that increasing \(P_{A}\) increases the overall system achievable data rate by helping to overcome interference and improve system performance. Increasing transmission power above a specific threshold, as specified in Figure 7 (7 dBm), is advantageous because it enhances the sent signal. This increase in signal strength aids in overcoming interference, improving the signal-to-noise ratio, and increasing the overall system's data rate. This boost in transmission power benefits both the proposed analytical and deep learning models, resulting in improved system performance even in low-interference conditions.