## 1 Introduction
This was achieved using the data produced by the Lagrange optimization technique to learn the model to predict CUE-IoTG, IoTG-CUE, BS-IoTD, and optimum IoTD-D distances for downlink and uplink data communication [11]. Deep learning is considered a strong candidate to solve many challenges that face the QoS enhancement of any wireless network system. Different interference control methods based on deep learning algorithms have been proposed in [11] and [18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28], to show how the implementation of deep learning algorithms enhances the system performance through different manners. Table 1 shows the difference between some of the recent deep learning interference control algorithms which enhance the system performance of different wireless network systems. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Interference Control Technique** & **Network Performance Achievement** \\ \hline \multirow{4}{*}{[11]} & Interference avoidance is based on determining the optimum uplink & \multirow{4}{*}{Enhances the system’s energy efficiency and throughput.} \\  & distance between IoTD-D and CUE-IoTG communication link. & \\  & determining the optimum downlink distances between BS-IoTD and & \\  & IoTG-CUE communication link. & \\ \hline \multirow{4}{*}{[18]} & A power allocation strategy is proposed based on extracting from the & \multirow{4}{*}{Maximizes global sum rate.} \\  & outdated channel information the local interference, this technique is & \\  & known as called interference feature extractor aided recurrent neural & \\  & network (IFE-RNN). & \\ \hline \multirow{4}{*}{[19]} & Each transmitter is equipped with deep reinforcement learning to make a & \multirow{4}{*}{Maximizes the sum rate.} \\  & different decision such as which user should be served and what the & \\  & needed transmission power to mitigate interference. & \\ \hline \multirow{4}{*}{[20]} & A multi-agent deep reinforcement learning (MADRL) is proposed to & \multirow{4}{*}{Improves the probability of successful} \\  & reduce co-channel interference, this technique is based on the selection of & \\  & autonomous channel and transmission power algorithms. & \\ \hline \multirow{4}{*}{[21]} & Captures interference across different links through a proposed PowerNet & \multirow{4}{*}{Enhances the system’s energy efficiency.} \\  & leverages convolutional layers. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between different recent proposed deep learning interference control for wireless networks. Many articles have been proposed to enhance the communication between vehicles and everything (V2X), yet this issue has room for further investigation. These investigations must focus on how to increase the system's reliability and efficiency for providing efficient road traffic information and enhancing road safety. Therefore, Table 2 shows the comparison between the recently proposed methods and their effect on the system performance. Additionally, based on the comparison between the proposed models, the novelty and the importance of the proposed approach appeared. The proposed model controls the power of any interference devices to assure that the data sent between any vehicle (V) and everything (X) is accurate and efficient. Based on the interference power control, the data received by any destination is supposed to be approximate without interference, which affects the received signal. Additionally, based on the proposed approach, the V2X system is evaluated in terms of achievable data rate, packet delivery ratio, packet loss rate, and average end-to-end delay under different channel conditions.