# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
## 4 Beyond 5G/6G Applications and Machine Learning
### Unsupervised Learning  
#### 4.2.1 Optimization Problems  
Coverage, power and capacity optimization are critical challenges in future 6G networks services [16]. In [68; 69], an unsupervised K-means algorithm is used to address the user selection and optimization of power allocation challenges in NOMA systems. Results show that the proposed model achieves great results in terms of accuracy and optimization. In [70], two Power Control (PC) algorithms, which are trained both using supervised and unsupervised learning, were proposed for Device-to-Device (D2D) scenarios. The comparison of the hybrid algorithms with conventional PC methods, show satisfactory results in terms of computational complexity, throughput, energy efficiency, resource allocation and \begin{table}
\begin{tabular}{p{128.0 pt} p{128.0 pt} p{128.0 pt} p{128.0 pt}} \hline \hline
**Paper** & **ML Approach** & **Application Problem** & **Description** \\ \hline
[48] & Support Tucker Machine & Fault detection & Accurately predicts faults/outliers, while retaining structure of big sensor data in IoT systems \\ \hline
[49] & DNN & Channel estimation & Effectively predicts channels and CSI \\ \hline
[50] & Deep DNN & Adaptive bit allocation & Accurately predicts system’s CSI in heterogeneous networks, reducing feedback overhead \\ \hline
[51] & knn \& SVC & Beam selection & Addresses beam selection in mm-wave communication systems as multi-class problem \\ \hline
[52] & SVM & Beam selection sum-rate & Achieves higher Average Sum Rate (ASR) with substantially lower computational complexity \\ \hline
[53] & DNN & Beam selection & Optimal beam selection to reduce space for initial beam, reducing beam overhead \\ \hline
[54] & DNN & Downlink beam prediction & Accurately predicts downlink beam in mm-wave systems, enhancing data rate \\ \hline
[55] & GRU & Beam prediction & Predicts BS and beam for each drone, extending their coverage leading to optimal beam prediction \\ \hline
[56] & ANN & Caching & Effectively addresses challenge of code caching \\ \hline
[57] & DNN & Caching & Optimizes caching in IoT systems \\ \hline
[58] & Decision Tree & Security & Boosts trust management using XAI for intrusion detection \\ \hline
[59] & LSTM & Security & Boosts accuracy using Nadam optimizer for intrusion detection \\ \hline
[60] & CNN & Security & Boosts accuracy for classification and detection of malware traffic \\ \hline
[61] & CNN \& ARN, RNN & Channel Estimation & Accurately predicts CSI in massive MIMO systems with channel aging property \\ \hline
[62] & Deep supervised mapping model & Channel Mapping & Addresses channel mapping in space and frequency domain for massive MIMO systems, reducing training and feedback overhead \\ \hline
[63] & CBTL, Deep CNN & Trajectory prediction & Maximizes network’s throughput by jointly optimizing cache and trajectory, then DCNN makes fast decisions online \\ \hline
[64] & ANN & Security & Detects GPS spoofing signals in UAV systems, reducing possible false alarms \\ \hline
[65] & SVM & Cyber Security & Detects jamming, spoofing and intrusion attacks in UAV systems \\ \hline
[66; 67] & Ensemble, ANN & RSS prediction & Accurately predicts RSS in UAV systems \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Supervised ML models in B5G/6G problems. power control optimization. This work is categorized in unsupervised ML, because for the approach the supervised decision tree occurs from the unsupervised Q-learning method, so for the final hybrid approach the most significant impact factor is the performance of the unsupervised model that defines the supervised phase of the model and so the final performance of the approach. Conventional approaches in modulation recognition of the received signals include several procedures such as preprocessing, classification and feature extraction. The authors in [71; 72] addressed the challenge of modulation recognition, by investigating the performance of different deep learning algorithms such as CNN, LSTM etc, by using unsupervised learning paradigms for optimization purposes.