## 5 ML Intersection with 6G Enabling Technologies
### ML and MEC Integration for Future 6G Communication Network  
A multi-stack RL algorithm was integrated with MEC for task, spectrum, and transit power allocation optimization [204]. The study addressed computational and transmission delay issues among users connected to a base station (BS). The multi-stack approach ensured that records of the existing resource allocation schemes are saved for reuse without the model repeating the learning process on old information. The proposed method facilitated a faster convergence rate and learning efficiency. The authors reported simulation results to yield over 18% gain in convergence speed and 11.1% gain in reduced delays for users. In another study, a distributed machine learning approach was integrated into a multiuser MEC for optimized performance in terms of offloading ratio, bandwidth, and computational ability [205]. The authors developed an FL method to improve the overall efficiency of the network. The framework allows for the parallel implementation of offloading and computing. The proposed method ensured better performance as it lowered the latency and energy consumption while allowing for more bandwidth and computational resources for important users. [PERSON] et al. [206] integrated ML, MEC, DT, and IoT technologies for the optimal performance of task offloading in the network. A DRL-based approach (DEETO) was proposed to achieve a dynamic task offloading scheme. DT techniques are utilized to enhance cooperation among the different IoT devices. By providing edge servers status information to the DEETO algorithm, the method effectively improved the energy efficiency of the system. In [PERSON] et al. [207], an IRS-assisted MEC technology was applied to optimize latency and energy consumption, while a DDPG-based ML algorithm was integrated for optimal system performance in terms of efficient resource allocation. The authors reported that the proposed DDPG algorithm achieved reduced total cost compared to the other local scheme. ML, UAV, and MEC systems converged for optimized secure transmission [208]. The authors first utilized a spiral placement algorithm to realize an effective deployment of UAV, and then a DRL was implemented to minimize the possibility of eavesdropping. Different \begin{table}
\begin{tabular}{c c c c} \hline
**Ref** & **Application** & **ML Model** & **Outcome** \\ \hline
[195] & Spectrum assignment & DRL & The model achieved improved system capacity and system throughput. \\ \hline
[196] & Security & LSTM & ML integration enhanced attack detection to an accuracy of 99\%. \\ \hline
[197] & Privacy preservation & QFL & Improved the data privacy and sensitivity of the data using FL integration with QC. \\ \hline
[198] & Security & RF & The selection protocol for QKD was optimized using RF algorithm integration, as the RF classifier significantly improved both preciseness and robustness with an accuracy of over 98\%. \\ \hline
[199] & Security & NN & Accurately and efficiently predicts the optimal parameters for a given QKD protocol. \\ \hline
[203] & Security & RF & Achieved over 99\% of the optimal secure key rate. \\ \hline
[201] & Parameter selection & BPNN & The BPNN integration achieved faster, and better optimization outcomes compared to the existing approaches. \\ \hline
[202] & Noise filtering & RNN & RNN integration achieved a reduction in noise power to about 15 dBm, while channel capacity was notably enlarged. \\ \hline
[200] & Security & SVR & Achieved optimal performance, minimized resource use, and ensured system stability. \\ \hline \end{tabular}
\end{table} 
Table 10: Summary of ML and QC integration for future 6G networks. schemes were tested for optimization, and the results show that the multi-agent method outperformed the other approaches and actualized overall system utility.