## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Interference Control Technique**} & \multicolumn{2}{c}{**Network Performance Achievement**} \\ \hline
[22] &
\begin{tabular}{c} Solves the problem of signal-to-interference-plus-noise by deep learning, \\ also formulates the joint design of interference coordination, power control, \\ and beamforming as a non-convex optimization problem. \\ \end{tabular} & Improves the system’s SINR and sum-rate capacity. \\ \hline
[23] &
\begin{tabular}{c} Replaces the traditional receiver with a deep receiver to be able to recover \\ the transmitted information from the distorted sent data. \\ \end{tabular} & Decreases received signal bit error rate (BER). \\ \hline
[24] & \
\begin{tabular}{c} Controls the downlink transmission power through a base station. \\ \end{tabular} &
\begin{tabular}{c} Enhance the system throughput and \\ decreases energy consumption. \\ \end{tabular} \\ \hline
[25] &
\begin{tabular}{c} Control the transmission power in the macro base station through Markov \\ decision process (MDP). \\ \end{tabular} & Maximizes sum spectrum efficiency. \\ \hline
[26] &
\begin{tabular}{c} Joint power control and channel selection optimization is implemented by \\ exploiting local information and outdated nonlocal information. \\ \end{tabular} & Maximizes system sum rate. \\ \hline
[27] &
\begin{tabular}{c} A power control algorithm based on multi-agent is proposed which is \\ designed to serve users by reusing the same spectrum band by deploying \\ multiple access points. \\ \end{tabular} & Enhance the overall networks sum rate. \\ \hline
[28] &
\begin{tabular}{c} A non-orthogonal multiple access (NOMA) downlink system based on \\ long-term resource allocation is formulated. \\ \end{tabular} & Improves the network utility, queuing delay, \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._ \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Implemented**} & \multicolumn{2}{c}{**Optimization**} & \multicolumn{2}{c}{**Deep Learning**} & \multicolumn{2}{c}{**System Metrics**} & \multicolumn{1}{c}{**Investigated**} \\
**Technique** & \multicolumn{2}{c}{**Problem**} & \multicolumn{2}{c}{**Method**} & \multicolumn{2}{c}{**Evaluation**} & \multicolumn{1}{c}{**Scenario**} \\ \hline
[4] & \begin{tabular}{c} Multi-hop relaying \\ between AV2X, direct \\ AV2X, and V2V \\ communications. \\ \end{tabular} & \begin{tabular}{c} Enhances system QoS \\ by finding the \\ optimum autonomous \\ inter-vehicle position \\ to communicate with \\ or to relay information \\ to any destination. \\ \end{tabular} & N/A & \
\begin{tabular}{c} Best relay-vehicle \\ position between AV2X. \\ PDR. \\ \end{tabular} &
\begin{tabular}{c} Relaying \\ communication \\ between AV2X \\ with direct \\ communication \\ \end{tabular} \\ \hline
[4] & \begin{tabular}{c} A combined \\ communication of V2I \\ with V2V is known as \\ cooperative \\ communication. \\ \end{tabular} & \begin{tabular}{c} Minimizes the overall \\ energy consumption \\ per bit given a target \\ for the outage \\ probability, \\ or maximizes the \\ system throughput. \\ \end{tabular} & \
\begin{tabular}{c} Energy consumption. \\ Throughput. \\ Packet delivery ratio. \\ \end{tabular} &
\begin{tabular}{c} Direct V2I \\ communication. \\ Multi-hop V2I \\ communication. \\ \end{tabular} \\ \hline
[6] & \begin{tabular}{c} A power control algorithm based on multi-agent is proposed which is \\ designed to serve users by reusing the same spectrum band by deploying \\ multiple access points. \\ \end{tabular} & \
\begin{tabular}{c} A power control algorithm based on multi-agent is proposed which is \\ multiple access points. \\ \end{tabular} &
\begin{tabular}{c} A power control algorithm based on multi-agent is proposed which is \\ multiple access points. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between the proposed approach and some different related works.