## 4 Discussion--Open Issues  
In Table 2, the indicative presented studies are categorized according to various 6G key-enabling technologies, in particular, AI/ML, DTs, open-air interfaces, network slicing, and CF approaches. From the discussion in the previous section, all current state-of-the-art approaches have adopted AI/ML solutions that are implemented in almost all considered architectural layers. AI/ML can contribute to various issues in the 6G context, such as resource optimization, threat prediction, and mitigation. With respect to resource optimization and in conjunction with other cutting-edge technologies, such as NFV and the organic 6G concept, the appropriate number of resources can be committed during service execution, which in turn leads to optimal resource management. These resources are not only related to hardware constrained nodes but also include optimum slice selection and configuration. Since 6G networks can change dynamically based on traffic conditions and overall user demands, in many of the presented works the concept of DRL was adopted. To this end, as previously mentioned, a mobile agent interacts with the surrounding environment and sends positive or negative feedback on certain decisions. In the same framework, since various potential misconfigurations can occur in 6G networks, the concept of DTs in parallel with ML has also been adopted. In this case, digital representations of the real-world environment assist in the examination of various scenarios that can be helpful for ML model training. To this end, transfer learning can also leverage fast decision-making, since knowledge of similar tasks is shared among the 6G network. One other issue is the ability of the network to collect a vast amount of data from heterogeneous resources. In this case, two major assumptions have mainly been followed \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Related Work**} & \multirow{2}{*}{**Main Concept**} & \multicolumn{6}{c}{**Key 6G-Enabling Technologies**} \\ \cline{3-8}  & & **AI/ML** & \begin{tabular}{c} **Digital** \\ **Twins** \\ \end{tabular} & \begin{tabular}{c} **Open** \\ **Interfaces** \\ \end{tabular} & \
\begin{tabular}{c} **Network** \\ **Slicing** \\ \end{tabular} &
\begin{tabular}{c} **Cell-Free** \\ **Approaches** \\ \end{tabular} \\ \hline
[55] & Organic 6G networks & & & \(\surd\) & & \\ \hline
[56] &
\begin{tabular}{c} Threat prediction and \\ mitigation \\ \end{tabular} & \(\surd\) & \(\surd\) & & & \\ \hline
[59] & Network slicing & & & & \(\surd\) & \\ \hline
[60] & Cell-free networks & & & & & \(\surd\) \\ \hline
[61] & 6G vision & \(\surd\) & & & & \\ \hline
[62] & Open RAN & \(\surd\) & & \(\surd\) & & \\ \hline
[63] &
\begin{tabular}{c} Multi-layered \\ architecture \\ \end{tabular} & \(\surd\) & \(\surd\) & & \(\surd\) & \\ \hline
[64] & 6G multi-layer vision & \(\surd\) & \(\surd\) & & & \\ \hline
[66] &
\begin{tabular}{c} 6G architectural \\ design based on DTs \\ \end{tabular} & & \(\surd\) & & & \\ \hline
[67] &
\begin{tabular}{c} Flexible layered \\ architecture \\ \end{tabular} & \(\surd\) & & & \(\surd\) & \\ \hline
[68] & Basic 6G trends & \(\surd\) & \(\surd\) & & \\ \hline
[70] &
\begin{tabular}{c} Self-evolving 6G \\ networks \\ \end{tabular} & \(\surd\) & & & \\ \hline
[71] &
\begin{tabular}{c} Deep reinforcement \\ learning in 6G \\ networks \\ \end{tabular} & \(\surd\) & & & \\ \hline
[72] & Building blocks for 6G & \(\surd\) & & & \(\surd\) \\ \hline
[73] & 6G project ANNA & \(\surd\) & \(\surd\) & & \\ \hline
[75] & Slicing concept in 6G & \(\surd\) & & \(\surd\) & \(\surd\) \\ \hline
[76] & 6G for smart cities & \(\surd\) & & & \\ \hline
[77] & 6G for smart cities & \(\surd\) & & & \\ \hline
[78] & 6G for smart cities & \(\surd\) & & & \\ \hline
[79] & Current 6G trends & \(\surd\) & & & \(\surd\) \\ \hline
[80] & PREDICT-6G & \(\surd\) & \(\surd\) & & \\ \hline
[81] &
\begin{tabular}{c} Advanced 6G use \\ cases \\ \end{tabular} & \(\surd\) & & & \(\surd\) & \\ \hline \hline \end{tabular}
\end{table}