## 1 Introduction
Enhancing the connectivity between V2X for efficient road traffic information and road safety is a challenge that should be tackled. Different environmental conditions such as interference, path loss, and signal-to-interference-plus-noise-ratio (_SINR_) can affect V2X communication. In this work, the connectivity between vehicles (V) and everything (X) along with its characteristics is investigated to enhance communication between V2X. Therefore, the main contributions of the proposed approach can be listed as the following:  
* An interference power control is developed through the proposed approach to enhance system performance in terms of achievable data rate, packet delivery ratio, packet loss rate, and average end-to-end delay for the V2X communication. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Implemented** & **Optimization** & **Deep Learning** & **System Metrics** & **Investigated** \\  & **Technique** & **Problem** & **Method** & **Evaluation** & **Scenario** \\ \hline \multirow{3}{*}{[7]} & Power allocation & Optimization of & & & & Cellular \\  & approach. & & energy efficiency. & N/A & Energy efficiency & D2D-based V2X communication network. \\ \hline \multirow{3}{*}{[8]} & Non-orthogonal & A joint-optimization & & & & \\  & multiple access & problem in terms of & & & mmWave-NOMA- \\  & (NOMA) into & user scheduling & N/A & semi-persistent & supported cellular \\  & semi-president & indicators and power & & scheduling (SPS) period. & V2X system based & on signal cell. \\  & scheduling. & & allocation. & & & \\ \hline \multirow{3}{*}{[13]} & A decentralized & A joint optimization & & & & Cellular V2X \\  & algorithm based on & problem in terms of & Deep & Sum Capacity. & communication in \\  & deep reinforcement & resource allocation & reinforcement & Satisfied rate. & a vehicular \\  & learning and Markov & and transmission & learning & & network. \\  & decision process. & mode selection. & & & \\ \hline \multirow{3}{*}{[14]} & A deep reinforcement & Maximizes energy & Deep & & & Cellular V2X \\  & learning for a & efficiency (EE) and & reinforcement & V2V energy efficiency. & Communication \\  & approach. & spectral efficiency (SE) & learning & & networks. \\ \hline \multirow{3}{*}{[15]} & Power control for two & Maximizes the & & & & \\  & different resource & limitation of the & & & \\  & allocation modes: & transmission power & & & V2X \\  & Scheduled resource & for both users & & & \\  & allocation model. & vehicular and & & & \\  & User equipment & pedestrian user & & & \\  & autonomous resource & equipment. & & & \\  & selection mode. & & & & \\ \hline \multirow{3}{*}{[16]} & Multi-hop relaying & & & Environmental & & \\  & strategy. & N/A & & recognition & & V2X \\  & & & (CNN). & improvement. & & communication \\  & strategy. & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \multirow{3}{*}{[17]} & Distributed congestion & & & & Channel busy ratio. & Cellular V2X \\  & control based on deep & & reinforcement & Packet delivery ratio. & communication \\  & reinforcement & & learning & Throughput. & networks. \\  & learning. & & & & \\ \hline \multirow{3}{*}{Proposed & Interference power & & & & \\  & control based on a & & & & \\  & distributed deep & & & & \\  & learning algorithm. & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Cont. * To ascertain that communication between V2X is reliable and efficient, an optimization problem using an analytical and deep learning model was developed. Based on the optimization model, the optimum required interference power (\(P_{I}\)) is obtained to fulfill the required system QoS. * A deep learning model is proposed and assessed, which employs 1D-CNN and dense layers, to calculate the required \(P_{I}\). The model can be employed in the design stage for interference power control prediction and circumvention in real-life applications.