## 5 ML Intersection with 6G Enabling Technologies
### ML and DT Integration for Future 6G Communication Network
The DT-assisted FL scheme enhanced the network performance of air-ground networks by enabling network privacy and data security. [PERSON] et al. [219] integrated DT and MEC into a three-layer FL framework in the heterogeneous cellular network scenario. In this framework, DT ensures that no synchronization of original data is attained while FL provides the user privacy protection. The authors then deployed a deep reinforcement learning-based approach to handle the joint optimization problems related to dynamic UD-station association and resource allocation. After simulations, the authors reported that the new scheme achieved a lowered task transmission failure rate, minimized energy consumption, and reduced communication cost compared with the existing baseline scheme. ML was incorporated into the design of a DT of a propagation environment that can enable the easy derivation and reporting of the channel state information (CSI) in a mobile communication system [220]. The authors combined the untrained neural networks (UNNs) and a conditional generative adversarial network (cGAN) algorithm for channel recreation on the digital twin. The proposed model achieved better signal to noise ratio (SNR), lowered overhead CSI reporting, and successfully modeled the wireless channel under different configurations. A DRL approach was leveraged to optimize the process and then incorporated into a digital twin technology design of a vehicular edge computing network [221]. The authors carried out analysis on real urban traffic datasets from the San Francisco Bay area, and the numerical results highlight the efficacy of ML-assisted DT-based EC networks in achieving optimized offloading costs when compared to the baseline approaches. Table 12 gives a summary of ML-integrated DT approaches for future wireless network improvement. DT has shown great promise in complimenting and improving the incoming 6G networks [117; 222]. We have highlighted important integrated approaches that incorporated ML for the enhancement and optimization of DT in network communications. Most applications have been for reporting channel state information (CSI), improving privacy and security, and lowering offloading costs in communication networks. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref** & **ML Algorithm** & **Application** & **Outcome** \\ \hline
[122] & LSTM & Handover parameter selection & The integrated approach resulted in a better handover rate (2.7\%) and faster convergence speed compared to conventional methods. \\ \hline
[217] & FL & Air-ground network optimization & The results show that the proposed approach enhanced system performance in terms of energy consumption and latency. \\ \hline
[218] & FL & Air-ground network optimization & ML-assisted approach achieved better overall system performance compared to the existing approaches. \\ \hline
[219] & DDPG and FL & User privacy protection & The developed model yielded reduced transmission time delay and minimum energy consumption. \\ \hline
[220] & UNNs and cGAN & Channel state information (CSI) & Achieved significant reduction in the task transmission failure rate and energy consumption. \\ \hline
[221] & DRL & Offloading optimization & The proposed scheme achieved optimized offloading costs when compared to the baseline approaches. \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Summary of ML and DT integration for optimized 6G wireless networks.