# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
## 4 Beyond 5G/6G Applications and Machine Learning
### Supervised Learning  
#### 4.1.1 Optimization Problems  
Coverage, power and capacity optimization are critical challenges in future 6G networks services [16]. In [37], Random Forest and knn algorithms are proposed to predict and optimize the Path Loss (PL). The results show a higher accuracy and reduced Mean Squared Error (MSE) compared with conventional approaches. The authors in [38] propose a novel approach, namely GRL, to address the problems of joint user association and power allocation. In the proposed model, for optimization purposes, the learning process is split into two parts, the generalization-representation learning (GRL) part, and the specialization-representation learning (SRL) part The authors assume a function that can represent the connection between the network's parameters and the optimal resource allocation, and problems are addressed by optimizing this selected function. In this approach, the data-driven (supervised learning) and model-driven (unsupervised learning) training methods are combined to accurately predict the optimal function and the results are satisfactory. In [39], a supervised ANN-based algorithm, named MLP-DBA, is proposed to predict the dynamic bandwidth allocation (DBA). The authors, aim to achieve bandwidth allocation close to optimal conventional approaches. The simulation results indicate that the proposed model can adaptively allocate the bandwidth, while improving the latency performance over the conventional DBA schemes. In [40], a DNN algorithm is proposed to predict the user's requirements in high dynamic (UAV) network. The results show better performance than the conventional Q-learning based algorithms that were mostly used. In [41], an RNN algorithm is proposed for intelligent load balancing. The proposed intelligent load balancer, named APRIL, can effectively load forecast information to maximize server utilization. Results show that the proposed forecasting model performs by between 5.88 and 92.6 better than the alternatives. The deviation in the performance is because the user's role greatly impacts the performance of the model. In [42], machine learning-based Cooperative Spectrum Sensing schemes (CSSs) have been proposed. In the proposed approaches, some nodes send the received signal power from the users to the Fusion Center (FC), where some artificial neural networks (ANNs) \begin{table}
\begin{tabular}{l l l l} \hline \hline
**ML Approach** & **Advantages** & **Limitations** \\ \hline \multirow{4}{*}{Q-learning} & \(\bullet\) & Learns directly the optimal policy & \(\bullet\) & Use of biased samples \\  & \(\bullet\) & Less computation cost & \(\bullet\) & High per-sample variance \\  & \(\bullet\) & Relatively fast & \(\bullet\) & Computationally expensive \\  & \(\bullet\) & Efficient for offline learning & \(\bullet\) & Not very efficient for online learning \\ \hline \multirow{2}{*}{SARSA} & \(\bullet\) & Fast & \(\bullet\) & Learns a near-optimal policy while \\  & \(\bullet\) & Efficient for online learning datasets & & exploring \\  & & & \(\bullet\) & Not very efficient for offline learning \\ \hline \multirow{2}{*}{Policy Gradient} & \(\bullet\) & Capable of finding best stochastic policy & \(\bullet\) & Slow convergence \\  & \(\bullet\) & Effective for high dimensionality datasets & \(\bullet\) & High variance \\ \hline \multirow{4}{*}{Actor Critic} & \(\bullet\) & Reduces variance with respect to pure policy & \\  & & methods & \(\bullet\) & Must be stochastic \\ \cline{1-1}  & \(\bullet\) & More sample efficient than other RL methods & \(\bullet\) & Estimators need high variance \\ \cline{1-1}  & \(\bullet\) & Guaranteed convergence & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Advantages and limitations of RL methods. and SVM approach are used to determine whether the channels are idle or not. ANN is used to recognize the transmit power while SVM is used to find the best decision boundary, acting as a classifier. The results show that proposed approaches can offer great results in terms of accuracy and performance.