# On End-to-End Intelligent Automation of 6G Networks
## 5 Research Innovation Opportunities for Intelligent Automation
### 6G Traffic Prediction  
Another crucial component that has a direct impact on the orchestration process is the incoming traffic. Accordingly, being able to predict and forecast network traffic can be a game changer. This is where ML can play a significant role by accurately predicting the type, volume, and time of the incoming traffic. If the prediction is accurate, NSPs can ensure that the end-users' requirements are satisfied by planning their resource allocation and service provisioning decisions in advance. Moreover, network performance can be improved and the corresponding operation costs can be significantly reduced by optimizing the network traffic aggregation process, which can fully depend on the accurate prediction of the incoming traffic. Simultaneously, the impact of network slicing and multi-tenancy needs to be considered, particularly  
* Ensuring traffic is separated between network slices and multi-tenant networks;
* Maintaining tenant authorization levels;
* Guaranteeing that the service level agreement requirements are met;
* Ensuring that data are isolated and authenticated.  
In addition to the above, it is important to have a better understanding of both legacy networks and 6G network traffic's characteristics, particularly given the continuously changing and evolving end-user behavior. Accordingly, accurate traffic prediction models can be developed that can become key components of 6G network orchestration frameworks.  
Figure 7 illustrates how ML can be incorporated at different levels within the network to enable accurate traffic prediction. For example, the ML-enabled traffic prediction module can be deployed near the network edge by allowing the different potential terrestrial and non-terrestrial 6G base stations (i.e., drones, satellites, roadside units, boats, etc.) to collect end-users'/devices' data, requesting different applications and services based on the network slice that they belong to. These base stations can then implement various supervised and semi-supervised ML models to predict the incoming traffic's type, volume, and time. Using this prediction, these base stations can then allocate the available wireless resources in a better and more effective manner. Moreover, these 6G network traffic predictions can be shared with the core network to better allocate the available resources at the transport level. In contrast, the ML-enabled traffic prediction module can be deployed at the 6G network core to reduce the computational complexity at the base stations (especially those with limited computing power, such as drones and roadside units). In this case, the ML-enabled traffic prediction module would collect the traffic generated by the different base stations to build the traffic prediction model. Then, this model can be used to better allocate the resources at the transport level, as well as assist base stations with their resource allocation decisions. The decision on where to deploy this traffic prediction module is determined by the NSPs' trade-off analysis between the computational complexity and the prediction granularity.  
Figure 6: Potential RL-enabled 6G network orchestration architecture.