This paper analyzes current standardization situation of 5G and the role network softwarization plays in order to address the challenges the new generation of mobile networks must face. This paper surveys recent documentation from the main stakeholders to pick out the use cases, scenarios and emerging vertical sectors that will be enabled by 5G technologies, and to identify future high-level service requirements. Driven by those service requirements 5G systems will support diverse radio access technology scenarios, meet end-to-end user experienced requirements and provide capability of flexible network deployment and efficient operations. Then, based on the identified requirements, the paper overviews the main 5G technology trends and design principles to address them. In particular, the paper emphasizes the role played by three main technologies, namely SDN, NFV and MEC, and analyzes the main open issues of these technologies in relation to 5G.

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This paper presents initial results available from the European Commission Horizon 2020 5G Public Private Partnership Phase 2 project "SaT5G" (Satellite and Terrestrial Network for 5G). 1 After describing the concept, objectives, challenges, and research pillars addressed by the SaT5G project, this paper elaborates on the selected use cases and scenarios for satellite communications positioning in the 5G usage scenario of enhanced mobile broadband.

5G envisages a "hyper-connected society" where an enormous number of devices are interconnected anywhere and at any time. Cloud-enabled radio access networks (RAN) where intelligence is placed in conjunction with the radio heads at the proximity of end users is a promising solution to fulfil the 5G expectations of sub-millisecond latency, huge traffic volumes and higher data rates. Network Functions Virtualization (NFV) and Software Defined Networking (SDN) developments enable end users to access advanced features such as configurability, automation, scalability, improved resource utilization and multi tenancy over the cloud-enabled RANs. Management and orchestration techniques are the ultimate factor that determine the effectiveness of the novel SDN/NFV features being introduced. Our focus in this study is the resource allocation in a realistic cloud-enabled RAN, taking into account the dynamics of ~100,000 persons movement in a crowded event, i.e. a football match. The proposed solution jointly orchestrates NFV and bandwidth resources, as one resource affects the other. Simulation results clearly verify the benefits of the proposed solution over traditional disjoint schemes.

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5G technologies promise to bring new network and service capacities and are expected to introduce significant architectural and service deployment transformations. The Cloud-Radio Access Networks (C-RAN) architecture, enabled by the combination of Software Defined Networking (SDN), Network Function Virtualization (NFV) and Mobile Edge Computing (MEC) technologies, play a key role in the development of 5G. In this context, this paper addresses the problems of Virtual Network Functions (VNF) provisioning (VNF-placement and service chain allocation) in a 5G network. In order to solve that problem, we propose a genetic algorithm that, considering both computing resources and optical network capacity, minimizes both the service blocking rate and CPU usage. In addition, we present an algorithm extension that adds a learning stage and evaluate the algorithm performance benefits in those scenarios where VNF allocations can be reconfigured. Results reveal and quantify the advantages of reconfiguring the VNF mapping depending on the current demands. Our methods outperform previous proposals in the literature, reducing the service blocking ratio while saving energy by reducing the number of active core CPUs.

Abstract:Current trends in broadband mobile networks are addressed towards the placement of different capabilities at the edge of the mobile network in a centralised way. On one hand, the split of the eNB between baseband processing units and remote radio headers makes it possible to process some of the protocols in centralised premises, likely with virtualised resources. On the other hand, mobile edge computing makes use of processing and storage capabilities close to the air interface in order to deploy optimised services with minimum delay. The confluence of both trends is a hot topic in the definition of future 5G networks. The full centralisation of both technologies in cloud data centres imposes stringent requirements to the fronthaul connections in terms of throughput and latency. Therefore, all those cells with limited network access would not be able to offer these types of services. This paper proposes a solution for these cases, based on the placement of processing and storage capabilities close to the remote units, which is especially well suited for the deployment of clusters of small cells. The proposed cloudenabled small cells include a highly efficient microserver with a limited set of virtualised resources offered to the cluster of small cells. As a result, a light data centre is created and commonly used for deploying centralised eNB and mobile edge computing functionalities. The paper covers the proposed architecture, with special focus on the integration of both aspects, and possible scenarios of application.

5G envisages a “hyperconnected society” where trillions of diverse entities could communicate with each other anywhere and at any time, some of which will demand extremely challenging performance requirements such as submillisecond low latency. Mobile Edge Computing (MEC) concept where application computing resources are deployed at the edge of the mobile network in proximity of an end user is a promising solution to improve quality of online experience. To make MEC more flexible and cost-effective Network Functions Virtualisation (NFV) and Software-Defined Networking (SDN) technologies are widely adopted. It leads to significant CAPEX and OPEX reduction with the help of a joint radio-cloud management and orchestration logic. In this paper we discuss and develop a reference architecture for the orchestration and management of the MEC ecosystem. Along with the lifecycle management flows of MEC services, indicating the interactions among the functional modules inside the Orchestrator and with external elements, QoS management with a focus on the channel state information technique is presented.