# On End-to-End Intelligent Automation of 6G Networks
## 1 Introduction
Moreover, concepts/paradigms such as cell-less networks, tactile internet, artificial intelligence (AI)-optimized wide-area networks, and the dynamic orchestration of personalized services will be crucial pillars in the successful realization of 6G. As a result, objects ranging from vehicles and industrial machines to wearable devices such as watches and apparel will learn to automatically adapt to our behavior, surrounding environments, and business requirements. Coupled with the expected benefits (both technological and financial) of developing 6G networks is a fresh set of challenges that need to be addressed and considered. One such challenge is the extremely stringent performance requirements stemming from the combined need for the remote control of devices/machines/entities, realization of augmented reality, and the presence of an immersive media experience [14]. This results in the need for extreme ultra-reliable low-latency (URLLC) performance as well as ultra-high rates of 100 Gbps or higher to facilitate the uncompressed transmission of high-quality 360-degree video. Another challenge is the energy efficiency of any technology, paradigm, or algorithm. This is because the network's performance will be dependent on the available energy in the respective architectural domain (human world, physical world, or digital world). As such, 6G network architectures require:  
* Programmable networks;
* Secure, robust, and reliable networks;
* Flexible deployment models;
* User-oriented services and adaptive architecture;
* Efficient and simple automated network orchestration. Among the many challenges that will arise when considering 6G networks, one emerging challenge is \"end-to-end network automation\" which includes orchestration as a crucial pillar. The Open Network Foundation defines orchestration as _\"the selection of resources to satisfy service demands in an optimal way, where the available resources, the service demands and the optimization criteria are all subject to change\"_[15]. In a similar fashion, the European Telecommunications Standards Institute defines orchestration as _\"the coordination of the resources and networks needed to set up cloud-based services and applications\"_[16]. In the context of 6G, network orchestration brings forward a variety of challenges due to the fact that such networks have to deal with more complex infrastructure and a diverse set of heterogeneous services and use cases. This includes fulfilling both the technical requirements (e.g., performance requirements in terms of throughput and latency, network isolation, etc.) and non-technical requirements (e.g., cost, sustainability, dependability, locality, etc.). As such, orchestration processes need to be more proactive compared to the previously developed reactive methodologies proposed for previous network generations. Moreover, the role of artificial intelligence (AI) and machine learning (ML) technologies as key enablers of predictive/proactive network slicing and orchestration is a paramount issue to consider. Accordingly, this paper aims at envisioning the role of different enabling technologies towards end-to-end intelligent automated 6G networks. To this end, this paper first reviews 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. Additionally, this paper presents 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 outlines 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. The remainder of this paper is organized as follows: Section 2 provides an overview of 6G networks and the corresponding enabling technologies.