# From 5G to 6G--Challenges, Technologies, and Applications
## 3 Sixth-Generation Network's Aspects
### Network Requirements
#### 3.2.2 Technologies
Although most of the currently deployed AI models are based on a centralized computation approach, i.e., in the cloud or the core network, a distributed AI and edge computing structure looks promising for consideration in 6G wireless systems. Moreover, building networks that support AI workflows on multiple levels can accelerate the use of AI in many ways, such as providing new models and data, which can help in achieving faster convergence rates with lower errors or prioritize and manage the connected end devices based on the application and the available resources. It is worth noting some of the challenges that DNC's implementation may face, such as the heterogeneity of the global network, resulting in the problem of integrating multiple sub-systems to work together efficiently, especially in vertical industries. For example, the compatibility between the different road vehicles and smart objects along the road is important in order to fully realize vehicle-to-vehicle (V2V), V2X, and smart objects' communications. _Edge computing_ is usually coupled with _network slicing_; the latter is considered to be crucial in 6G networks as different end devices require vastly different network metrics. Each set of nodes can be grouped into a network slice; for example, V2V/V2X communication requires extremely low-latency connections compared to smart homes; thus, each communication type will be grouped into different network slices with different qualities of service. Communication links and transceivers are bounded by upper and lower transmission energy costs. These limits are directly related to the ICs' rated power, BER constraints, data rates, and channel interference [81; 82]. Sixth-generation networks are expected to bring record energy cost efficiency. This can be achieved by utilizing AI in data bits' modulation and OWC, as mentioned earlier. This can translate into smaller communication nodes, a less expensive overall communication cost, and giant complex networks. Moreover, integrating pervasive AI models with 6G networks, especially at the edge networking components' and end devices' levels, can contribute heavily to achieving advanced personalized network security and better network key performance indicators [83]. However, security in 6G will still be one of the challenging areas due to increasing threats arising from using new technologies [84]. For example, quantum communication will require special mechanisms to protect quantum encryption keys, which are different from the conventional methods used currently in protecting encryption keys for RF communications. Another example is related to massive AI deployment in 6G networks, where unauthorized DL sessions can take place in the network by intruders or eavesdroppers. This calls for developing new methods of user identification and authorization in the network. As networks evolved to become what they are today by offering a myriad of emerging services and applications, such as lightning-fast communication speeds for end users, support for Industry 4.0, VR/AR, and many more, space-air-ground integrated networks (SAGINs) have emerged as a focal research area recently. This sheds light on the idea of vertically unifying the different network components, which are heterogeneous by nature, to achieve unprecedented network coverage, enhancing the quality of service (QoS), and granting access to a ubiquitous spectrum of services and application [85]. For example, the work in [86] gave a comprehensive study and survey of SAGINs and their role in 6G. It laid out a technical insight into the architecture, requirements, and use cases of SAGINs in 6G. In parallel, researchers are exploring in depth the edge computing and AI fields to match the needs of the ever-expanding networks and their roles in SAGINs [87]. Edge nodes can be incorporated into SAGINs as heterogeneous network controllers to handle network information collection, monitoring, and control, mobility and radio resources' management, and content caching.