# On End-to-End Intelligent Automation of 6G Networks
## 6 Conclusions  
The digital transformation of businesses and services is now in full force, particularly given the current deployment efforts of 5G networks. This has opened the world to a new set of unique challenges and opportunities driven by the need for communication technologies and architectures that enable growth and sustainability while also promoting the digital transformation at hand. Here, 6G promises to be the set of technologies and architectures that achieves this goal by offering the means to interact and control the digital and virtual worlds that are decoupled from their physical location. Thus, 6G networks and architectures need to allow for the human experience to be expanded across the physical, biological, and digital worlds. To this end, a new set of enabling technologies (e.g., mmWave and THz communication, intelligent reflective surfaces, non-terrestrial and special purpose networks, and edge AI, among others) will be introduced for 6G in addition to those currently developed/deployed for 5G networks. Additionally, new concepts/paradigms that will emerge need to be considered, such as cell-less networks, tactile internet, and artificial intelligence (AI)-optimized wide-area networks.  
As a result, a fresh set of challenges will emerge to enable the achievement of the desired 6G benefits. One such challenge is that of \"end-to-end network automation\". This is because such networks have to deal with more complex infrastructure and a diverse set of heterogeneous services and use cases. Therefore, orchestration processes need to be more proactive compared to the previously developed reactive methodologies adopted by previous network generations. Moreover, it is crucial to consider the role of AI and ML technologies as key enablers of predictive/proactive network slicing and orchestration.  
To this end, this paper provided a vision on the role of different enabling technologies towards end-to-end intelligent automated 6G networks. Accordingly, this paper first reviewed the literature focusing on the orchestration and automation of next-generation networks by discussing in detail the challenges facing the efficient and effective management and orchestration of 6G Networks. This includes challenges such as continuous orchestration, heterogeneous service orchestration, and AI-driven orchestration, among others. Moreover, this paper presented some of the research opportunities for innovation within this area that aim at addressing the research gaps and tackling the aforementioned challenges. More specifically, it outlined how advanced data-driven paradigms such as reinforcement learning and federated learning can be incorporated into 6G networks for more dynamic, efficient, effective, and intelligent network automation and orchestration. This includes the use of RL for dynamic data-driven orchestration, ML for 6G traffic prediction at both the edge and core levels, and FL for distributed orchestration decision-making.  
Conceptualization, A.M.; Project administration, A.S.; Supervision, A.S. and A.A.-D.; Visualization, A.M.; Writing--original draft preparation, A.M.; Writing--review and editing, A.S. and A.A.-D. All authors have read and agreed to the published version of the manuscript. This research received no external funding.