# From 5G to 6G--Challenges, Technologies, and Applications
## 3 Sixth-Generation Network's Aspects
### Network Requirements
#### 3.2.2 Technologies
For example, parts of the aerial network can be transferred to edge servers; for example, a group of UAVs can be deployed for distributed computing, when resources are limited in ground terminals, or to mitigate backhaul transmission congestion. Edge computing UAVs can also support pioneering services, such as vehicular VR/AR gaming or road sign recognition. It is also possible to deploy satellites as edge nodes; this is helpful in many scenarios, such as in Earth observation, where image processing can take place on board for critical real-time applications. Some of these applications are space junk capturing, infrastructure monitoring, and disaster relief, leading to a reduction in time and savings on bandwidth. Moving on to AI's role in SAGINs, it is safe to say that native edge AI is forecasted to play a major part in SAGINs. One of the main points that involves edge AI is facilitating learning the network characteristics for traffic pattern and vehicle movement prediction and efficient packet routing, and this is of great interest, as many of SAGINs' components are dynamic by nature, such as UAVs and satellites. Moreover, edge AI plays a crucial role in content caching by optimizing content placement and delivery parameters on the different network nodes and links. Additionally, due to the heterogeneity of SAGINs and the contrasting QoS requirements, it becomes a hard task to perform resource allocation in the conventional way; thus, edge AI can assist by performing service load prediction and network slicing optimization based on the QoS requirements. Edge AI can also help satellites in object recognition tasks, whether space debris or structures on Earth's surface. However, there remain some challenges in the context of this discussion, such as that edge AI requests a considerable amount of data, which can be arduous to store and access in SAGINs on demand, as well as being costly to implement. The training of AI models can also incur a noticeable latency or delay that might not be tolerable in some applications, such as satellites performing real-time space junk avoidance. Recent advances in information and communication technology (ICT) and AI have been stimulating the deployment of more and more devices at the edge of networks. It is expected that there will be over 75 billion sensors and edge devices by 2025 [88]. Besides, this has motivated the appearance of new applications, which request instant decision-making with minimal transmission delay. Examples of such applications include autonomous driving, XR for the Metaverse, facial recognition and sentiment analysis for digital twin profile construction, sensors' communication in factories and plants for a fully automated production process, and UAVs to facilitate SAGINs. To address these emergent challenges for next-generation networks, edge-native AI, a novel technology that intrinsically combines edge computing and AI, has been widely regarded as a predominant research area to provide on-the-spot processing and analysis of data generated at the edge of the network [89; 90]. Its importance stems from the following facts: (i) future networks will be human-centric, that is to say they will rely heavily on shared human sensory data to offer personalized services; (ii) due to the immense amount of data generated at the edge of networks by the new generation of devices and latency-sensitive applications, the advent of new techniques will improve the efficiency of the networks at the edge; (iii) shifting data traffic and its processing from the cloud to the edge of the network reduces the load on backhaul links and potential decision latency; (iv) edge-native AI is expected to offer unprecedented intelligence in resource sharing by deploying real-time predictive algorithms. Therefore, it is envisioned that future networks (e.g., 6G) will consolidate edge-native AI within their core, thereby playing an essential role in optimizing key performance indicators (KPIs) in terms of energy and spectral efficiency, throughput, and communication latency and reliability.