Although the radio access network (RAN) part of mobile networks offers a significant opportunity for benefiting from the use of SDN ideas, this opportunity is largely untapped due to the lack of a software-defined RAN (SD-RAN) platform. We fill this void with FlexRAN, a flexible and programmable SD-RAN platform that separates the RAN control and data planes through a new, custom-tailored southbound API. Aided by virtualized control functions and control delegation features, FlexRAN provides a flexible control plane designed with support for real-time RAN control applications, flexibility to realize various degrees of coordination among RAN infrastructure entities, and programmability to adapt control over time and easier evolution to the future following SDN/NFV principles. We implement FlexRAN as an extension to a modified version of the OpenAirInterface LTE platform, with evaluation results indicating the feasibility of using FlexRAN under the stringent time constraints posed by the RAN. To demonstrate the effectiveness of FlexRAN as an SD-RAN platform and highlight its applicability for a diverse set of use cases, we present three network services deployed over FlexRAN focusing on interference management, mobile edge computing and RAN sharing.

/pdf/flexran-a-flexible-and-programmable-platform-for-software-2zstc78egp.pdf

FlexRAN: A Flexible and Programmable Platform for Software-Defined Radio Access Networks

5G cellular networks are expected to be the key infrastructure to deliver the emerging services. These services bring new requirements and challenges that obstruct the desired goal of forthcoming networks. Mobile operators are rethinking their network design to provide more flexible, dynamic, cost-effective and intelligent solutions. This paper starts with describing the background of the 5G wireless networks then we give a deep insight into a set of 5G challenges and research opportunities for machine learning (ML) techniques to manage these challenges. The first part of the paper is devoted to overview the fifth-generation of cellular networks, explaining its requirements as well as its key technologies, their challenges and its forthcoming architecture. The second part is devoted to present a basic overview of ML techniques that are nowadays applied to cellular networks. The last part discusses the most important related works which propose ML solutions in order to overcome 5G challenges.

A survey of 5G network systems: challenges and machine learning approaches

The deployment of 5G networks will necessarily involve the installation of new base station (BS) equipment to support the requirements of next-generation mobile services. In a scenario where there exist already many sources of electromagnetic fields (EMFs), including overlapping 2G/3G/4G technologies of competing network operators, there is a growing concern that the planning of a 5G network will be severely constrained by the limits on maximum EMF levels established in a wide set of regulations. The goal of this paper is to shed light on EMF-aware 5G network planning and, in particular, on the problem of site selection for 5G BS equipment that abides by downlink EMF limits. To this end, we present the current state of the art in EMF-aware mobile networking and overview the current exposure limits and how the EMF constraints may impact 5G planning. We then substantiate our analysis by reporting on two realistic case studies, which demonstrate the saturation of EMF levels already occurring under current 2G/3G/4G networks, as well as the negative impact of strict regulations on network planning and user quality of service. Finally, we discuss the expected impact of 5G technologies in terms of EMFs and draw the guidelines for an EMF-aware planning of 5G. Our analysis suggests that the EMF-aware 5G planning risks to be a real challenge for network operators, which stimulates further actions at governmental, societal, technological, and research levels.

Planning 5G Networks Under EMF Constraints: State of the Art and Vision

Nowadays, at least two billion people are experiencing a complete lack of wireless cellular network coverage. These users live in rural areas and low-income regions, where network operators are not keen to invest, mainly due to high capital expenditure and operational expenditure costs, as well as the scarcity of electricity from the grid. We tackle this challenge by proposing a 5G network explicitly designed to serve rural and low-income areas. Our solution investigates the possibility of mounting remote radio heads on top of unmanned aerial vehicles, as well as large cells (LCs) to increase the coverage range. In addition, 5G nodes are powered by solar panels and batteries. Preliminary results, obtained over three representative case studies located in Italy, Cook Islands, and Zimbabwe, show that providing connectivity in rural and low-income areas by means of the proposed 5G architecture is feasible. At the same time, we also show that the monthly subscription fee paid by the users can be kept sufficiently low, that is, less than €1/month in low-income areas, and around €11/month in rural regions.

Bringing 5G into Rural and Low-Income Areas: Is It Feasible?

Fifth-Generation (5G) mobile cellular networks provide a promising platform for new, innovative and diverse IoT applications, such as ultra-reliable and low latency communication, real-time and dynamic data processing, intensive computation, and massive device connectivity. End-to-End (E2E) network slicing candidates present a promising approach to resource allocation and distribution that permit operators to flexibly provide scalable virtualized and dedicated logical networks over common physical infrastructure. Though network slicing promises the provision of services on demand, many of its use cases, such as self-driving cars and Google’s Stadia, would require the integration of a Multi-Access Edge Computing (MEC) platform in 5G networks. Edge Computing is envisioned as one of the key drivers for 5G and Sixth-Generation (6G) mobile cellular networks, but its role in network slicing remains to be fully explored. We investigate MEC and network slicing for the provision of 5G service focused use cases. Recently, changes to the cloud-native 5G core are a focus with MEC use cases providing network scalability, elasticity, flexibility, and automation. A cloud-native microservices architecture, along with its potential use cases for 5G network slicing, is envisioned. This paper also elaborates on the recent advances made in enabling E2E network slicing, its enabling technologies, solutions, and current standardization efforts. Finally, this paper identifies open research issues and challenges and provides possible solutions and recommendations.

/pdf/cloud-native-network-slicing-using-software-defined-4pr7jni8bm.pdf

Cloud-Native Network Slicing Using Software Defined Networking Based Multi-Access Edge Computing: A Survey

/pdf/5g-ppp-architecture-working-group-view-on-5g-architecture-3qg8xbcqbr.pdf

5G PPP Architecture Working Group: View on 5G Architecture

We propose the innovative architecture of Superfluidity, a Horizon 2020 project, co-funded by the European Union. Superfluidity targets 5G networks, by addressing key network operator challenges with a multi-pronged approach, based on the concept of a flexible, highly adaptive, superfluid network. Superfluidity supports rapid service deployment and migration in a heterogeneous network environment, regardless of the underlying hardware. The overall proposal offers advanced capabilities in terms of service deployment and interoperability, while guaranteeing high-performance levels end-to-end. Copyright © 2016 John Wiley & Sons, Ltd.

Superfluidity: a flexible functional architecture for 5G networks

Most media streaming services are composed by different virtualized processing functions such as encoding, packaging, encryption, content stitching etc. Deployment of these functions in the cloud is attractive as it enables flexibility in deployment options and resource allocation for the different functions. Yet, most of the time overprovisioning of cloud resources is necessary in order to meet demand variability. This can be costly, especially for large scale deployments. Prior art proposes resource allocation based on analytical models that minimize the costs of cloud deployments under a quality of service (QoS) constraint. However, these models do not sufficiently capture the underlying complexity of services composed of multiple processing functions. Instead, we introduce a novel methodology based on full-stack telemetry and machine learning to profile virtualized or cloud native media processing functions individually. The basis of the approach consists of investigating 4 categories of performance metrics: throughput, anomaly, latency and entropy (TALE) in offline (stress tests) and online setups using cloud telemetry. Machine learning is then used to profile the media processing function in the targeted cloud/NFV environment and to extract the most relevant cloud level Key Performance Indicators (KPIs) that relate to the final perceived quality and known client side performance indicators. The results enable more efficient monitoring, as only KPI related metrics need to be collected, stored and analyzed, reducing the storage and communication footprints by over 85%. In addition a detailed overview of the functions behavior was obtained, enabling optimized initial configuration and deployment, and more fine-grained dynamic online resource allocation reducing overprovisioning and avoiding function collapse. We further highlight the next steps towards cloud native carrier grade virtualized processing functions relevant for future network architectures such as in emerging 5G architectures.

Automated profiling of virtualized media processing functions using telemetry and machine learning

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5G PPP Architecture Working Group: View on 5G Architecture

Superfluidity: a flexible functional architecture for 5G networks

Automated profiling of virtualized media processing functions using telemetry and machine learning