# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
## 4 Beyond 5G/6G Applications and Machine Learning
### Reinforcement Learning
#### 4.3.8 Security  
In [122], a DRL is proposed to maximize throughput, and security metrics against jamming attacks, in 6G network. Simulation results show that the proposed approach is robust against jamming and can achieve throughput enhancement, compared with conventional policies. In [123], the authors use a Markov model to deal with several ad  
\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
[96] & NAAC & Spectrum allocation & Uses information from user’s neighbors and improves sum rate in D2D uses cases \\ \hline
[97] & GAN-DDQN &
\begin{tabular}{l} Spectrum allocation per network \\ slice \\ \end{tabular} & Uses a deep Q-learning based approach to optimize spectrum allocation \\ \hline
[98] & Q-learning & Resource allocation &
\begin{tabular}{l} Mijmizes outage probability of information by \\ assigning the channel resources, while satisfying \\ average power constraint at the energy harvesting \\ node \\ \end{tabular} \\ \hline
[99] & Q-learning & Channel selection &
\begin{tabular}{l} Achieves higher detection probability and accuracy, \\ reduces scanning overhead and access delay \\ \end{tabular} \\ \hline
[100] & Deep Q-learning & Energy consumption &
\begin{tabular}{l} In cooperative communications, model selects \\ optimal relay from different nodes without needing \\ network model, reducing consumption with lower \\ convergence time \\ \end{tabular} \\ \hline
[101] & Deep Q-learning & Dynamic power allocation &
\begin{tabular}{l} Each transmitter exploits its neighbors to collect CSI \\ and QoS information and then adapt its needed \\ transmit power \\ \end{tabular} \\ \hline
[102] & RL-CAT, RL-pCAT & Data rate &
\begin{tabular}{l} Achives data rate improvements both in uplink and \\ downlink directions \\ \end{tabular} \\ \hline
[103] & DRL-based approach & Resource management &
\begin{tabular}{l} Makes intelligent decisions on UE communications \\ and minimizes system’s power consumption \\ \end{tabular} \\ \hline
[105] & Actor-critic & \
\begin{tabular}{l} Resource allocation, joint \\ user control \\ \end{tabular} &
\begin{tabular}{l} Reduces data rate assigned to each IoT network and \\ IoT device,chooses whether transmission will be in \\ space or terrestrial network. \\ \end{tabular} \\ \hline
[106] & SAQ-learning, BLA-MAQ algorithm & \
\begin{tabular}{l} Resource allocation, offloading \\ decision \\ \end{tabular} &
\begin{tabular}{l} Uses historical experience and achieves optimal \\ accuracy in various network scenarios \\ \end{tabular} \\ \hline
[107] & MDP-based algorithm & Caching &
\begin{tabular}{l} Leads to resource allocation optimization with low \\ complexity in cache-aided MEC networks \\ \end{tabular} \\ \hline
[108] & Deep actor-critic & Caching &
\begin{tabular}{l} Used for centralized and decentralized uses cases, \\ optimizing cache hit rate and transmission delay \\ \end{tabular} \\ \hline
[109] & MAMAB & Caching &
\begin{tabular}{l} Learns online the caching strategy in both stationary and non-stationary environments \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 8: RL models in 6G optimization and caching problems.  
vanced jamming attacks. When dealing with attacks such as swept jamming and dynamic jamming, the authors model a multi-agent reinforcement learning (MARL) algorithm for effective defense. The simulation results show that the algorithm can effectively avoid these advanced jamming attacks, thanks to collaboratively sharing the spectrum to its agents. In [104], a novel DRL-based algorithm is proposed to ensure secure beamforming approach against eavesdroppers in dynamic IRS-aided environments. The model uses post-decision state (PDS) and prioritized experience replay (PER) approaches to boost the learning efficiency and secrecy performance of the system. The proposed novel approach can significantly improve the system secrecy rate and QoS (thus optimal beamforming is required) in IRS-aided secure communication systems.