# From 5G to 6G--Challenges, Technologies, and Applications
## 3 Sixth-Generation Network's Aspects
### Network Requirements
#### 3.2.2 Technologies
In particular, the network resources are limited; thus, it is vital to integrate AI within the network to boost the performance while maintaining high network efficiency and capacity levels. This means much of the future network is going to be underpinned by AI and deep learning (DL) algorithms to bring balance and stability among all network nodes. In other words, AI will be integrated in the underlying fabric of networks and will act as an anchor for designing, deploying, and optimizing networks. For instance, the chip manufacturer, Nvidia, very recently announced a cloud-AI video streaming platform called Maxine [58] to reduce the bandwidth needed for video calls to one-tenth without any reduction in quality. Moreover, Nvidia's latest graphical processing unit (GPU) lineup, the 3000 series, features enhanced AI for constructing a higher number of frames per second with enhanced details for video games, and this will directly enhance the virtual reality experience, which will be featured heavily in 6G. Another significant use for AI in networks can be found in integrating convolutional neural networks for autonomous modulation classification [59]. This gains its importance from the varying end user requirements and applications while maintaining a high spectrum efficiency. However, there are a few challenges to address such as implementing secure distributed AI models with reasonable network complexity for autonomous systems. Network complexity will keep increasing exponentially with time, and this is arguably the result of many activities such as network densification, multi-tier heterogeneous base stations' deployment, AI integration, and softwarization. Oneway to deal with high network complexity, especially in smart facilities such as smart factories [60], is by apportioning a section of the network to Internet of Things (IoT) devices' communication for coordination, self-organization, and optimization of their operational methods in, for example, conveyor systems. We are also interested here in examining a number of DL techniques, as they will be the power house for a magnitude of services in 6G networks. Two of the main candidates are supervised DL based on deep neural networks and deep reinforcement learning (DRL), which can combine theoretical models and real-world data on latency and reliability to fulfill the stringent network requirements. In detail, they can approximate the optimal resource allocation policy and predict traffic and mobility using state-decision pairs acquired from optimization algorithms or recorded data [61]. Additionally, beam selection is an important measure when operating under high frequencies, such as the ones required by 6G networks. Thus, deep neural networks can also be used for beam selection with the incorporation of the power delay profile of the channel into the model [62]. Another pivotal DL technique in 6G networks can be pinpointed, which is the deep Q-network based on reinforcement learning (RL). It can be used to optimize multi-layer radio resources in challenging scenarios, such as unmanned aerial vehicles' (UAVs) deployment [63]. The discussion can be expanded further to include the long short-term memory DL method, which can be utilized in integrated networks (refer to F in Figure 5). This method can be used in predicting energy harvesting in the network. The importance of this stems from energy harvesting's role in reducing the economic and environmental footprint of 6G communication systems, as discussed earlier [64]. We list a summary of potential promising DL techniques to be used in 6G networks in Table 1.  
Another concept on the rise as a promising key technology in 6G is hybrid networking. Hybrid layering refers to de-constructing the structure of the conventional network communication protocols and layers to be more modular and versatile based on the service the network needs to provide [65].