Smart Contracts in the 5G Roaming Architecture: The Fusion of Blockchain with 5G Networks
Abstract: The roll-out of the fifth generation of cellular network (5G) technology has generated a new surge of interest in the potential of blockchain to automate various use cases involving cellular networks. 5G is indeed expected to offer new market opportunities for small and large enterprises alike. In this article, we introduce a new roaming network architecture for 5G based on a permissioned blockchain platform with smart contracts. The proposed solution improves the visibility for mobile network operators of their subscribers’ activities in the visited network, as well as enabling quick payment reconciliation and reducing fraudulent transactions. The paper further reports on the methodology and architecture of the proposed blockchain-based roaming solution using the Hyperledger platform.This work has been performed within the EU’s H2020 projects 5G-CARMEN (825012), and 5G-ZORRO (871533) and funded through a collaborative program between the University of Bologna and the Fondazione Bruno Kessler.
Authors: Babak Mafakheri, University of Bologna, Fondazione Bruno Kessler; Andreas Heider-Aviet, Deutsche Telekom 5G Program; Roberto Riggio, RISE Research Institutes of Sweden AB (former I2CAT); Leonardo Goratti, Safran Passenger Innovations GmbH.
5GPPPTechnology Board Working Group5G-IA’s Trials Working Group, Edge Computing for 5GNetworks
Abstract: This whitepaper presents a rationale on why and how 5G can benefit from Edge Computing; a review on how 5GPPP projects have been using and enhancing Edge Computing for 5G and beyond systems. The 5G PPP Initiative and the 5GIA are happy to present a new white paper entitled “Edge Computing for 5G Networks”. This white paper provides a) a brief introduction to the Edge computing concept, b) an exhaustive technology review focusing on virtualisation, orchestration, network control, and operational frameworks, c) a discussion about the role of security, and d) an analysis of several business aspects around the Edge ecosystem. Moreover, the white paper provides an in-depth analysis of Edge solutions that have been selected, deployed and validated by 17 different EU funded 5G PPP projects. Read the 5GZORRO contribution.
Centralized and Federated Learning for Predictive VNF Autoscaling in Multi-domain 5G Networks and Beyond
Abstract: Network Function Virtualization (NFV) and Multi-access Edge Computing (MEC) are two technologies expected to play a vital role in 5G and beyond networks. However, adequate mechanisms are required to meet the dynamically changing network service demands to utilize the network resources optimally and also to satisfy the demanding QoS requirements. Particularly in multi-domain scenarios, the additional challenge of isolation and data privacy among domains needs to be tackled. To this end, centralized and distributed Artificial Intelligence (AI)-driven resource orchestration techniques (e.g., virtual network function (VNF) autoscaling) are foreseen as the main enabler. In this work, we propose deep learning models, both centralized and federated approaches, that can perform horizontal and vertical autoscaling in multi-domain networks.
Authors: Tejas Subramanya(Nokia Bell Labs); Roberto Riggio (I2CAT)
AI-driven Zero-touch Operations, Security and Trust in Multi-operator 5G Networks: a Conceptual Architecture
Abstract: The 5G network solutions currently standardised and deployed do not yet enable the full potential of pervasive networking and computing envisioned in 5G initial visions: network services and slices with different QoS profiles do not span multiple operators; security, trust and automation is limited. The evolution of 5G towards a truly production-level stage needs to heavily rely on automated end-to-end network operations, use of distributed Artificial Intelligence (AI) for cognitive network orchestration and management and minimal manual interventions (zero-touch automation). All these elements are key to implement highly pervasive network infrastructures. Moreover, Distributed Ledger Technologies (DLT) can be adopted to implement distributed security and trust through Smart Contracts among multiple non-trusted parties. In this paper, we propose an initial concept of a zero-touch security and trust architecture for ubiquitous computing and connectivity in 5G networks. Our architecture aims at cross-domain security & trust orchestration mechanisms by coupling DLTs with AI-driven operations and service lifecycle automation in multi-tenant and multi-stakeholder environments. Three representative use cases are identified through which we will validate the work which will be validated in the test facilities at 5GBarcelona and 5TONIC/Madrid.
Authors: Gino Carrozzo (Nextworks); M. Shuaib Siddiqui (I2CAT); August Betzler (I2CAT); José Bonnet (Altice Labs); Gregorio Martinez Perez (Univ. Murcia); Aurora Ramos (ATOS); Tejas Subramanya (Fondazione Bruno Kessler)
Adaptive ML-based Frame Length Optimisation in Enterprise SD-WLANs
Abstract: Software-Defned Networking (SDN) is gaining a lot of traction in wireless systems with several practical implementations and numerous proposals being made. Despite instigating a shift from monolithic network architectures towards more modulated operations, automated network management requires the ability to extract, utilise and improve knowledge over time.Machine Learning (ML) is evolving from a simple tool applied in networking to an active component in what is known as Knowledge-Defned Networking (KDN). This work discusses the inclusion of ML techniques in the specifc case of Software-Defned Wireless Local Area Networks (SD-WLANs), paying particular attention to the frame length optimization problem.This work proposes an adaptive MLbased approach for frame size selection on a per-user basis by taking into account both specifc channel conditions and global performance indicators. The approach has been gauged by analysing a multitude of scenarios, with the results showing an average improvement of 18.36% in goodput over standard aggregation mechanisms.
Authors: Estefanía Coronado (Fondazione Bruno Kessler); Abin Thomas (FBK); Roberto Riggio (former FBK)
aiOS: An Intelligence Layer for SD-WLANs
Abstract: Software-Defined Networking (SDN) promises to deliver a more manageable network whose behaviour could be easily changed using applications written in high-level declarative languages running on top of a logically centralized control plane resulting, on the one hand, in the mushrooming of complex point solutions to very specific problems and, on the other hand, in the creation of a multitude of network configuration options. This fact is especially true for 802.11-based Software-Defined WLANs (SD-WLANs). It is our standpoint that to tame this increase in complexity, future SD-WLANs must follow an Artificial Intelligence (AI) native approach.This paper presents aiOS, an AI-based Operating System for SD-WLANs. The aiOS is used to implement several Machine Learning (ML) models for user-adaptive frame length selection in SD-WLANs.An extensive performance evaluation carried out on a real-world testbed shows that this approach improves the aggregated network throughput by up to 55%. The entire implementation is released including the controller, the ML models, and the programmable data-path under a permissive license for academic use.
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