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Notably, developing an innovative architectural network paradigm is essential to address the technical challenging of 5G applications' requirements in a unified platform. Forthcoming applications will provide a wide range of networking, computing and storage capabilities closer to the endusers. In this context, the 5G-PPP Phase two project named "5G-CORAL: A 5G Convergent Virtualized Radio Access Network Living at the Edge" aims at identifying and experimentally validating which are the key technology innovations allowing for the development of a convergent 5G multi-RAT access based on a virtualized Edge and Fog architecture being scalable, flexible and interoperable with other domains including transport, core network and distant Clouds. In 5G-CORAL, an architecture is proposed based on ETSI MEC and ETSI NFV frameworks in a unified platform. Then, a set of exemplary use cases benefiting from Edge and Fog networks in near proximity of the end-user are proposed for demonstration on top of connected car, shopping mall and high-speed train platforms.

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.

This paper focuses on techniques or actions that the network operator should consider for the mobile network's evolution towards 5G from a sustainability point of view. To this end, the energy consumption of the Radio Access Network (RAN) is assessed when considering different load conditions (obtained by means of actual daily traffic profiles extracted from the live network) and different yearly traffic forecasts. An overview of different "what-if" scenarios towards 2020 and beyond is provided and energy consumption of the network is assessed by considering evolutionary Power Models (PMs) of future Base Stations (BSs) related to different Radio Access Technologies (RATs). Together with evolutionary PMs, many Energy Efficiency (EE) features that may be implemented within the network are also considered, with particular focus on EE features evaluated at higher time scale (from minutes to years), i.e., related to progressive network renewal with traffic steering options and legacy RATs' phase-off policies. These have been evaluated by using a software tool fed by actual traffic profiles, aiming at assessing EE performance at network level, by also considering the European Telecommunications Standards Institute (ETSI) EE specification ES 203 228, which is based on homogeneous clusters' evaluations that may be extrapolated at country level, providing useful information on the possible evolutions of the mobile networks in the view of future 5G systems.

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