# From 5G to 6G--Challenges, Technologies, and Applications
## 3 Sixth-Generation Network's Aspects
### Network Requirements
#### 3.2.2 Technologies
\begin{table}
\begin{tabular}{l l} \hline \hline
**DL Method** & **Potential Use** \\ \hline MD-IMA [70] & Focuses on designing an intelligent situation-aware resource allocation technique for multi-dimensional intelligent multiple access (MD-IMA). \\ \hline \multirow{2}{*}{AOW-DQN [71]} & Reuces on designing an intelligent situation-aware resource allocation technique for multi-dimensional intelligent multiple access (MD-IMA). \\  & The deep learning (DL) framework is based on long short-term memory (LSTM) and deep reinforcement learning (DRL). \\ \hline \multirow{2}{*}{AOW-DQN [71]} & Building a machine learning (ML-)-based architecture for the 6G Industrial Internet of Things (IoT) and improved learning efficiency by modifying the observation window size to respond to the industrial environment’s dynamics via an novel adaptive observation window for deep Q-network. \\ \hline \multirow{2}{*}{Micro-Safe [72]} & Maintaining customized safety services to the end users in 6G intelligent transportation systems to minimize the rate of accidents via developing algorithms based on a deep neural network (DNN) that would enhance the accuracy of the decisions to be presented to the end users. \\ \hline \multirow{2}{*}{DDPG [73]} & In the 6G RAN, a slicing control strategy is performed through the DRL framework based on the twin-imcessie Markov decision. The developed algorithm is based on the convergence of the double deep-Q-network (Double-DQN) and the deep deterministic policy gradient (DDPG). \\ \hline \multirow{2}{*}{FAT-DL [74]} & Developed for massive device detection in 6G networks by using a feature-aided adaptive-tuning deep learning (FAT-DL) network. It is based on a layer-by-layer training design that uses the trained data to decide the distribution parameters of the devices in the network. \\ \hline \multirow{2}{*}{DL [75]} & Developed for connected autonomous vehicles in 6G networks, DL combined with stochastic network calculus is used to train on the data for the fast calculation of the delay limits in real-time operations, which helps in cooperative driving. \\ \hline \multirow{2}{*}{DRLR [76]} & In 6G IoT networks, unmanned aerial vehicles (UAVs) can be used to collect data from sensors. UAV route planning algorithms can be developed using the DRL recruitment (DRLR) scheme. The data collection process is improved by reducing the cost and enhancing the coverage area. \\ \hline \multirow{2}{*}{Scaler [77]} & In 6G Internet of Everything ( IoE) systems, IScaler, a technique based on DRL, is utilized for resource scaling and service placement, especially for mobile edge computing. It offers improved scaling and placement decisions and overcomes the dynamic environment challenges. \\ \hline \multirow{2}{*}{DRL [78]} & In 6G optical wireless communication (OWC) systems, the handover problem can be resolved efficiently using a DRL-based framework for smooth and uninterrupted access point switching for end users. DRL utilizes the Q-target and the Q-evaluation to train and update the neutral network. \\ \hline \multirow{2}{*}{H-DAC-RL [79]} & In massive 6G space–terrestrial integrated IoT systems, network control and resource allocation can be performed through hierarchical deep actor -critic RL (H-DAC-RL), where the policy function is considered as the “actor” and the value function is named the “critic”. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: An overview of promising deep learning methods in 6G networks. Distributed network computations (DNCs) can satisfy the different end devices' requirements and applications. For instance, critical applications that are delay sensitive can be served via the geographically closest network component (such as an edge node) instead of the core network, or non-urgent data can be filtered out before network core processing; this is also known as _edge computing_[80].