## 5 ML Intersection with 6G Enabling Technologies
### ML and DT Integration for Future 6G Communication Network  
For optimal handover parameter application, [PERSON] et al. [122] explore the prospect of a DT-enabled DQN handover parameter selection method. The authors reported that the proposed LSTM-assisted digital twin effectively improved the performance of the handover parameter optimization in terms of its system efficiency and overall convergence. The simulation results showed that the DT and ML integrated approach produced a better handover rate (2.7%), faster convergence speed, and overall better performance compared to the conventional method. A lightweight DT architecture is proposed for improving the network performance of air-ground networks [217]. In this study, the authors deployed FL to enhance the DT modeling task across several distributed ground devices. Furthermore, a distributed algorithm was implemented to solve privacy issues with FL. The results \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref** & **Application** & **ML Model** & **Outcome** \\ \hline \multirow{3}{*}{[210]} & Task, spectrum, & \multirow{3}{*}{A multiple-stack RL} & Improved convergence speed by 18\% and the maximal delay among \\  & and transmit & & users by 11.1\%. \\ \hline \multirow{3}{*}{[205]} & \multirow{3}{*}{Data offloading} & FL & The developed method effectively lowered the system’s energy \\  & & & consumption and cost. \\ \hline \multirow{3}{*}{[206]} & \multirow{3}{*}{Task offloading} & DEETO algorithm & The novel approach achieved optimized energy usage and reduced \\  & & & instability of workload in the edge server. \\ \hline \multirow{3}{*}{[207]} & Confidential task offloading & \multirow{3}{*}{DDPG} & The proposed DDPG algorithm achieved lower total cost. \\ \hline \multirow{3}{*}{[208]} & \multirow{3}{*}{Secure offloading} & Single-agent and & \multirow{3}{*}{\begin{tabular}{} \end{tabular} } \\  & & multi-agent & \\  & reinforcement learning & & achieved overall better system utility. \\ \hline \multirow{3}{*}{[209]} & \multirow{3}{*}{Secure offloading} & \multirow{3}{*}{DDPG} & The proposed approach achieved better performance compared with \\  & & the other optimization approaches. \\ \hline \multirow{3}{*}{[204]} & Trajectory & \multirow{3}{*}{MADDPG} & The proposed approach reduced energy consumption and enhanced \\  & Planning & & the UAV’s trajectory and offloading decision. \\ \hline \multirow{3}{*}{[211]} & Efficient & \multirow{3}{*}{HT3O} & The new approach improved the convergence efficiency and drastically \\  & Scheduling & & lowered the complexity of the system. \\ \hline \multirow{3}{*}{[212]} & Efficient task & \multirow{3}{*}{DRL-based RAT} & The RAT approach outperformed all the other tested approaches. \\  & offloading & & \\ \hline \multirow{3}{*}{[213]} & \multirow{3}{*}{Path Planning} & DDQN-based & \multirow{3}{*}{\
\begin{tabular}{} \end{tabular} } \\  & & throughput than conventional algorithms. \\ \hline \multirow{3}{*}{[215]} & Computational & \multirow{3}{*}{HGT and DRL} & The proposed models enabled convergence at the desired point for \\  & offloading & & structural stability. \\ \hline \multirow{3}{*}{[216]} & Resource & \multirow{3}{*}{MADDPG} & The proposed model attained quick convergence, higher delay, and \\  & management & & better QoS than the other tested schemes. \\ \hline \multirow{3}{*}{[214]} & Dynamic resource allocation & Naive Bayes, KNN, & \multirow{3}{*}{
\begin{tabular}{} \end{tabular} } \\  & Random Forest, J48, & \\ \cline{1-1} \cline{4-4}  & SVM, and ZeroR. & & the QoS and QoE of end users. \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Summary of ML and MEC integration for future 6G applications. showed that the developed lightweight DT model performed better in lowering energy consumption, reducing time delay, and improving model accuracy. A similar study has also realized improvement in accuracy and energy saving compared to conventional approaches in air-ground network applications [218]; however, the authors deployed a Stackelberg game-based incentive mechanism for the FL.