# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
## 4 Beyond 5G/6G Applications and Machine Learning
### Reinforcement Learning  
#### 4.3.1 Optimization Problems  
In [96], the authors propose a multi-agent deep reinforcement learning-based model, named Neighbor-Agent Actor Critic (NAAC), for spectrum allocation in 6G network D2D scenarios. This model uses information from user's neighbors for centralized training and utilizes any cooperation between the users to optimize system's performance. The simulation results show that the proposed approach can improve the sum rate of D2D links and have good convergence. In [97], a deep Q-learning based approach is proposed, namely a Generative Adversarial Network-powered Deep Distributional Q Network (GAN-DDQN) for spectrum allocation per network slice. Simulation results show enhanced performance accuracy compared with conventional deep Q-learning algorithms. In [98], the authors propose a reinforcement Q-learning-based algorithm, for resource allocation. The model minimizes the outage probability of information by assigning the channel resources. The results demonstrate the superior performance and effectiveness of the proposed scheme while satisfying the average power constraint at the energy harvesting node. In [99], the authors propose a Q-learning based algorithm for channel selection, scanning the order of the channel and so reducing the overhead and possible delays. The proposed approach achieves higher detection probability and accuracy, and reduction of scanning overhead and access delay when compared with state-of-the-art algorithm, resulting to enhanced spectrum sharing. In [100], a deep Q network based algorithm is proposed for cooperative communications in 6G networks. The model aims to select optimal relay from different nodes without needing a network model. Results show that the proposed algorithms can achieve better performance probability, and reduced energy consumption with lower convergence time than existing approaches. In [101], a deep RL-based algorithm is developed for dynamic power allocation. Each transmitter exploits its neighbors to collect CSI and QoS information and then adapt its needed transmit power. Random variations and delays in the CSI are addressed using deep Q-learning based approach. The proposed algorithm is shown to achieve near-optimal power allocation results based on delayed CSI measurements and is excellent for scenarios where the CSI is significant. In [102], novel reinforcement learning-based transmission approaches, named Reinforcement Learning Channel-aware Transmission (RL-CAT) and Reinforcement Learning pCAT (RL-pCAT), for data rate optimization are proposed. The proposed models significantly outperform conventional probabilistic approaches and achieve data rate improvements of up to 181 in uplink and up to 270 in downlink transmission direction. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Paper** & **ML Approach** & **Application Problem** & **Description** \\ \hline [88] & Unsupervised GMM & Security & Enhances physical layer security \\ \hline [89] & Unsupervised CNN,SAE & Intrusion detection & Accurately detects intrusion \\ \hline [91] & k-means clustering \& Q-learning & Clustering & Spatially clusters correlated users and places the UAV in 3-D manner \\ \hline [92] & MLP, LSTM & UAV location & Predicts optimal UAV location and optimizes user throughout and system performance \\ \hline [93] & DNN & Sum rate & Maximization of sum-rate in a MIMO single base station system, while improving considerably the computational speed \\ \hline [94] & k-means, CAPD & VLC & Reduces non-linearity in VLC and multi-band VLC systems. CAPD was also applied as pre-distorter, with great performance improvement \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._In [103], a DRL-based approach for joint mode selection and resource management is proposed. Each user equipment (UE) can operate either in cloud RAN (C-RAN) mode or D2D mode. The network controller makes intelligent decisions on UE communications and aims to minimize system's power consumption.