6G Vision, Value, Use Cases and Technologies from European 6G Flagship Project Hexa-X

While 5G is being deployed and the economy and society begin to reap the associated benefits, the research and development community starts to focus on the next, 6th Generation (6G) of wireless communications. Although there are papers available in the literature on visions, requirements and technical enablers for 6G from various academic perspectives, there is a lack of joint industry and academic work towards 6G. In this paper a consolidated view on vision, values, use cases and key enabling technologies from leading industry stakeholders and academia is presented. The authors represent the mobile communications ecosystem with competences spanning hardware, link layer and networking aspects, as well as standardization and regulation. The second contribution of the paper is revisiting and analyzing the key concurrent initiatives on 6G. A third contribution of the paper is the identification and justification of six key 6G research challenges: (i) “connecting”, in the sense of empowering, exploiting and governing, intelligence; (ii) realizing a network of networks, i.e., leveraging on existing networks and investments, while reinventing roles and protocols where needed; (iii) delivering extreme experiences, when/where needed; (iv) (environmental, economic, social) sustainability to address the major challenges of current societies; (v) trustworthiness as an ingrained fundamental design principle; (vi) supporting cost-effective global service coverage. A fourth contribution is a comprehensive specification of a concrete first-set of industry and academia jointly defined use cases for 6G, e.g., massive twinning, cooperative robots, immersive telepresence, and others. Finally, the anticipated evolutions in the radio, network and management/orchestration domains are discussed

D4.1 Draft air interface harmonization and user plane design

The METIS-II project envisions the design of a new air interface in order to fulfil all the performance requirements of the envisioned 5G use cases including some extreme low latency use cases and ultra-reliable transmission, xMBB requiring additional capacity that is only available in very high frequencies, as well as mMTC with extremely densely distributed sensors and very long battery life requirements. Designing an adaptable and flexible 5G Air Interface (AI), which will tackle these use cases while offering native multi-service support, is one of the key tasks of METIS-II WP4. This deliverable will highlight the challenges of designing an AI required to operate in a wide range of spectrum bands and cell sizes, capable of addressing the diverse services with often diverging requirements, and propose a design and suitability assessment framework for 5G AI candidates.Aydin, O.; Gebert, J.; Belschner, J.; Bazzi, J.; Weitkemper, P.; Kilinc, C.; Leonardo Da Silva, I.... (2016). D4.1 Draft air interface harmonization and user plane design. https://doi.org/10.13140/RG.2.2.24542.0288

Architecture landscape

The network architecture evolution journey will carry on in the years ahead, driving a large scale adoption of 5th Generation (5G) and 5G-Advanced use cases with significantly decreased deployment and operational costs, and enabling new and innovative use-case-driven solutions towards 6th Generation (6G) with higher economic and societal values. The goal of this chapter, thus, is to present the envisioned societal impact, use cases and the End-to-End (E2E) 6G architecture. The E2E 6G architecture includes summarization of the various technical enablers as well as the system and functional views of the architecture

MEC Support for Network Slicing: Status and Limitations from a Standardization Viewpoint

Edge computing and network slicing might be considered as main pillars of the upcoming 5G systems as they inject flexibility in the network management operations. While one prominent architectural framework for edge computing has been recently defined by the ETSI standard organization, namely Multi-access Edge Computing (MEC), network slicing has reached its momentum by fostering interest in different standardization bodies and fora. To better understand how such distinct network slicing definitions impact on the standardized MEC framework, ETSI has recently published a study on the matter. In this paper, we first overview with a comprehensive analysis the different network slicing concepts and their relationship. Then, we elaborate on the ETSI study to provide an integrated view of network slicing technology within the context of MEC. Finally, we report on the open challenges in the ETSI study and we propose two solutions to evolve the current MEC framework towards end-to-end multi-slice support and efficient multi-tenant inter-slice communication in 5G deployments.This work has been partially funded by the EU H2020 projects 5G-CARMEN (grant no. 825012), 5Genesis (grant no. 815178) and the H2020 collaborative EU/TW research project 5G-DIVE (grant no. 859881)

Effective Goal-oriented 6G Communications: the Energy-aware Edge Inferencing Case

International audienceCurrently, the world experiences an unprecedentedly increasing generation of application data, from sensor measurements to video streams, thanks to the extreme connectivity capability provided by 5G networks. Going beyond 5G technology, such data aim to be ingested by Artificial Intelligence (AI) functions instantiated in the network to facilitate informed decisions, essential for the operation of applications, such as automated driving and factory automation. Nonetheless, while computing platforms hosting Machine Learning (ML) models are ever powerful, their energy footprint is a key impeding factor towards realizing a wireless network as a sustainable intelligent platform. Focusing on a beyond 5G wireless network, overlaid by a Multi-access Edge Computing (MEC) infrastructure with inferencing capabilities, our paper tackles the problem of energy-aware dependable inference by considering inference effectiveness as value of a goal that needs to be accomplished by paying the minimum price in energy consumption. Both MEC-assisted standalone and ensemble inference options are evaluated. It is shown that, for some system scenarios, goal effectiveness above 84% is achieved and sustained even by relaxing communication reliability requirements by one decimal digit, while enjoying a device radio energy consumption reduction of almost 23% at the same time. Also, ensemble inference is shown to improve system-wide energy efficiency and even achieve higher goal effectiveness, as compared to the standalone case for some system parameterizations

5G innovations for new business opportunities

5G is the next generation mobile network that enablesinnovation and supports progressive change across allvertical industries and across our society1. Through itsRadio Access Network (RAN) design and its orchestratedend-to-end architecture, it has the potential toboost innovation and generate economic growthin the European economy. The 5G service modelssupport agility and dynamicity, thereby impacting thegranularity, duration and trustworthiness of businessrelationships. The ability to combine private and publicnetworks and data centres across multiple domains ina secure and controlled way facilitates collaborativebusiness processes. It reshapes the digital businessecosystem with new value chains linking stakeholdersfrom the telecommunications world and the verticalindustries in win-win situations. New stakeholdersemerge in this evolved ecosystem, for example cloudcompanies and software houses that profit from thecloudification and virtualization of the infrastructure,and brokers that facilitate sharing of spectrum andtrading of connectivity and processing resources. Smalland medium-sized enterprises and start-ups are able toembed 5G in their innovative products and services forexisting and new customers and markets, leveraging onthe Anything as a Service (XaaS) model

Federated Learning of Explainable AI Models in 6G Systems: Towards Secure and Automated Vehicle Networking

This article presents the concept of federated learning (FL) of eXplainable Artificial Intelligence (XAI) models as an enabling technology in advanced 5G towards 6G systems and discusses its applicability to the automated vehicle networking use case. Although the FL of neural networks has been widely investigated exploiting variants of stochastic gradient descent as the optimization method, it has not yet been adequately studied in the context of inherently explainable models. On the one side, XAI permits improving user experience of the offered communication services by helping end users trust (by design) that in-network AI functionality issues appropriate action recommendations. On the other side, FL ensures security and privacy of both vehicular and user data across the whole system. These desiderata are often ignored in existing AI-based solutions for wireless network planning, design and operation. In this perspective, the article provides a detailed description of relevant 6G use cases, with a focus on vehicle-to-everything (V2X) environments: we describe a framework to evaluate the proposed approach involving online training based on real data from live networks. FL of XAI models is expected to bring benefits as a methodology for achieving seamless availability of decentralized, lightweight and communication efficient intelligence. Impacts of the proposed approach (including standardization perspectives) consist in a better trustworthiness of operations, e.g., via explainability of quality of experience (QoE) predictions, along with security and privacy-preserving management of data from sensors, terminals, users and applications

Federated Learning of Explainable AI Models in 6G Systems: Towards Secure and Automated Vehicle Networking

This article presents the concept of federated learning (FL) of eXplainable Artificial Intelligence (XAI) models as an enabling technology in advanced 5G towards 6G systems and discusses its applicability to the automated vehicle networking use case. Although the FL of neural networks has been widely investigated exploiting variants of stochastic gradient descent as the optimization method, it has not yet been adequately studied in the context of inherently explainable models. On the one side, XAI permits improving user experience of the offered communication services by helping end users trust (by design) that in-network AI functionality issues appropriate action recommendations. On the other side, FL ensures security and privacy of both vehicular and user data across the whole system. These desiderata are often ignored in existing AI-based solutions for wireless network planning, design and operation. In this perspective, the article provides a detailed description of relevant 6G use cases, with a focus on vehicle-to-everything (V2X) environments: we describe a framework to evaluate the proposed approach involving online training based on real data from live networks. FL of XAI models is expected to bring benefits as a methodology for achieving seamless availability of decentralized, lightweight and communication efficient intelligence. Impacts of the proposed approach (including standardization perspectives) consist in a better trustworthiness of operations, e.g., via explainability of quality of experience (QoE) predictions, along with security and privacy-preserving management of data from sensors, terminals, users and applications