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openaire: EC/H2020/101016509/EU//CHARITY | openaire: EC/H2020/101069732/EU//aerOS Artificial Intelligence and Machine Learning (AI/ML) can transform mobile communications, enable new applications and services, and pave the way beyond 5G. The adoption of AI/ML may also advance network optimizations and service life-cycle management. This article provides an overview of AI/ML analytics exploring the main concepts in relation to network automation and service-based architectures. Furthermore, it sheds light on AI/ML analytics service enablers by considering a system-level approach elaborating on the key technologies related to service discovery, request, control, and reporting of AI/ML analytics. Finally, it provides an analysis for maintaining the services of AI/ML analytics up-to-date, by considering modifications of the AI/ML model in order to detect, interpret, and compensate for potential performance drifts.

This white paper is one of the twelve new themed 6G White Papers led by the 6G Flagship program. It involved the participation of more than 50 experts and enthusiasts of future 6G technologies. In this white paper, we intend to shed light on advanced features relevant to networking that would shape the evolution beyond 5G, ultimately leading to the 6G mobile system.

openaire: EC/H2020/761816/EU//global5g.org Network slicing has been identified as the backbone of the rapidly evolving 5G technology. However, as its consolidation and standardization progress, there are no literatures that comprehensively discuss its key principles, enablers and research challenges. This paper elaborates network slicing from an end-to-end perspective detailing its historical heritage, principal concepts, enabling technologies and solutions as well as the current standardization efforts. In particular, it overviews the diverse use cases and network requirements of network slicing, the pre-slicing era, considering RAN sharing as well as the end-to-end orchestration and management, encompassing the radio access, transport network and the core network. This paper also provides details of specific slicing solutions for each part of the 5G system. Finally, this paper identifies a number of open research challenges and provides recommendations towards potential solutions.

openaire: EC/H2020/723172/EU//5GPagoda Multi-access edge computing (MEC) is an emerging ecosystem, which aims at converging telecommunication and IT services, providing a cloud computing platform at the edge of the radio access network. MEC offers storage and computational resources at the edge, reducing latency for mobile end users and utilizing more efficiently the mobile backhaul and core networks. This paper introduces a survey on MEC and focuses on the fundamental key enabling technologies. It elaborates MEC orchestration considering both individual services and a network of MEC platforms supporting mobility, bringing light into the different orchestration deployment options. In addition, this paper analyzes the MEC reference architecture and main deployment scenarios, which offer multi-tenancy support for application developers, content providers, and third parties. Finally, this paper overviews the current standardization activities and elaborates further on open research challenges.

The emerging network slicing paradigm for 5G provides new business opportunities by enabling multi-tenancy support. At the same time, new technical challenges are introduced, as novel resource allocation algorithms are required to accommodate different business models. In particular, infrastructure providers need to implement radically new admission control policies to decide on network slices requests depending on their Service Level Agreements (SLA). When implementing such admission control policies, infrastructure providers may apply forecasting techniques in order to adjust the allocated slice resources so as to optimize the network utilization while meeting network slices’ SLAs. This paper focuses on the design of three key network slicing building blocks responsible for (i) traffic analysis and prediction per network slice, (ii) admission control decisions for network slice requests, and (iii) adaptive correction of the forecasted load based on measured deviations. Our results show very substantial potential gains in terms of system utilization as well as a trade-off between conservative forecasting configurations versus more aggressive ones (higher gains, SLA risk). TRUE pub