# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
## 5 Open Issues  
ML application can offer new research directions and solutions in wireless communication systems and also support the realization of 6G wireless communication networks and services. Although significant research has emerged on the field of ML in wireless communication systems, there are still many challenges and open issues to be resolved:  
* Time Convergence: A careful investigation of the relatively long convergence time of ML methods, as well as the factors that influence the convergence, is needed. Optimizing the time convergence is critical, as long ML time convergence can undermine the performance in highly dynamic wireless networks [127]. * Resource allocation: AI-enabled networks also impact e-health applications. For instance, advancing outside-of-clinic operations by using wearable sensor requires harmonizing network resource allocation across several technologies, and ML can be helpful for such harmonization [127]. * QoS and QoE: A network encompassing a large and diverse set of users will have very dynamic operation, as users may have very different QoS and QoE requirements. For example, users require high throughput and low delay in video stream applications, in the expense of security, but when it comes to payment software, the users demand high security, even in the expense of throughput. In this direction, a design of a \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Paper** & **ML Approach** & **Application Problem** & **Description** \\ \hline \(\left[116\right]\) & Offline RL model & Handover & Optimizes handover decisions by studying prolonged user’s connectivity \\ \hline \(\left[117\right]\) & DRL & Handover, timing & Uses camera images for predicting future data rate of mm-wave links and ensuring that proactive Handover is performed \\ \hline \(\left[118\right]\) & Distributed RL model & Handover & Minimizes Handover inn mm-wave systems, reducing signal overhead \\ \hline \(\left[119\right]\) & Novel RL model & Resource allocation & Satisfies latency constraints on V2V links and is able to minimize any interference in the V2V system \\ \hline \(\left[120\right]\) & Distributed Resource allocation model, Double DQN & Resource allocation, Sum rate & Modeled as multi-agent system, while jointly training agents maximizing sum-rate and reducing latency \\ \hline \(\left[121\right]\) & Two-stage DRL & Jointcontent placement, trajectory design & Maximize users hit rate while constraining cache capacity, tracks online mobile users while maintaining energy constrains \\ \hline \(\left[122\right]\) & DRL & Security & Robust against jamming attacks, achieves throughput enhancement \\ \hline \(\left[123\right]\) & MARL & Security & Deals with advanced jamming attacks (swept jamming and dynamic jamming) by collaboratively sharing the spectrum to its agents \\ \hline \(\left[104\right]\) & Novel RL model & Security & Uses PDS and PER approaches to boost learning efficiency and secrecy performance in dynamic IRS-aided environments \\ \hline \(\left[124\right]\) & Multi-agent multi-user DQN & Power allocation & Capable of multi-hoping in VLC/RF systems and addressing each AP as agent, boosting convergence rate and achieving optimal power allocation \\ \hline \(\left[125\right]\) & Multi-agent Q-learning & Power allocation & Optimizes transmit power at Aps, ensuring QoS satisfaction and optimal power allocation in VLC/RF systems \\ \hline \(\left[126\right]\) & Deep Q-learning & Fault detection & Detects diagnoses faults, using less features and achieving great accuracy \\ \hline \hline \end{tabular}
\end{table} 
Table 9: _Cont._cross-layer, action based ML protocol for different applications is a critical issue, as to meet various requirement while balancing network resources [128]. * UAVs as an Intelligent Service(UaaIS): UaaIS employs UAVs to intelligently provide fundamental services in terms of wireless communication, edge computing, and edge caching, using advanced ML techniques.