NLP Powered Intent Based Network Management for Private 5G Networks

Intent driven networking holds the promise of simplifying network operations by allowing operators to use declarative, instead of imperative, interfaces. Adoption of this technology for 5G and beyond networks is however still in its infancy, where the required architectures, platforms, interfaces and algorithms are still being discussed. In this work, we present the design and implementation of a novel intent based platform for private 5G networks powered by a Natural Language Processing (NLP) interface. We demonstrate how our platform simplifies network operations in three relevant private network use cases, including: i) an intent based slice provisioning use case, ii) an intent based positioning use case, and iii) an intent based service deployment use case. Finally, all use cases are benchmarked in terms of intent provisioning time.European Commission’s Horizon 2020 871428, 5G-CLARIT

Asynchronous Time-Sensitive Networking for Industrial Networks

Time-Sensitive Networking (TSN) is expected to be a cornerstone in tomorrow’s industrial networks. That is because of its ability to provide deterministic quality-of-service in terms of delay, jitter, and scalability. Moreover, it enables more scalable, more affordable, and easier to manage and operate networks compared to current industrial networks, which are based on Industrial Ethernet. In this article, we evaluate the maximum capacity of the asynchronous TSN networks to accommodate industrial traffic flows. To that end, we formally formulate the flow allocation problem in the mentioned networks as a convex mixed-integer non-linear program. To the best of the authors’ knowledge, neither the maximum utilization of the asynchronous TSN networks nor the formulation of the flow allocation problem in those networks have been previously addressed in the literature. The results show that the network topology and the traffic matrix highly impact on the link utilization.This work has been partially funded by the H2020 research and innovation project 5G-CLARITY (Grant No. 871428), national research project TRUE5G: PID2019-108713RB-C5

5G Infrastructure Network Slicing: E2E Mean Delay Model and Effectiveness Assessment to Reduce Downtimes in Industry 4.0

This work has been partially funded by the H2020 project 5G-CLARITY (Grant No. 871428) and the Spanish national project TRUE-5G (PID2019-108713RB-C53).Fifth Generation (5G) is expected to meet stringent performance network requisites of the Industry 4.0. Moreover, its built-in network slicing capabilities allow for the support of the traffic heterogeneity in Industry 4.0 over the same physical network infrastructure. However, 5G network slicing capabilities might not be enough in terms of degree of isolation for many private 5G networks use cases, such as multi-tenancy in Industry 4.0. In this vein, infrastructure network slicing, which refers to the use of dedicated and well isolated resources for each network slice at every network domain, fits the necessities of those use cases. In this article, we evaluate the effectiveness of infrastructure slicing to provide isolation among production lines (PLs) in an industrial private 5G network. To that end, we develop a queuing theory-based model to estimate the end-to-end (E2E) mean packet delay of the infrastructure slices. Then, we use this model to compare the E2E mean delay for two configurations, i.e., dedicated infrastructure slices with segregated resources for each PL against the use of a single shared infrastructure slice to serve the performance-sensitive traffic from PLs. Also we evaluate the use of Time-Sensitive Networking (TSN) against bare Ethernet to provide layer 2 connectivity among the 5G system components. We use a complete and realistic setup based on experimental and simulation data of the scenario considered. Our results support the effectiveness of infrastructure slicing to provide isolation in performance among the different slices. Then, using dedicated slices with segregated resources for each PL might reduce the number of the production downtimes and associated costs as the malfunctioning of a PL will not affect the network performance perceived by the performance-sensitive traffic from other PLs. Last, our results show that, besides the improvement in performance, TSN technology truly provides full isolation in the transport network compared to standard Ethernet thanks to traffic prioritization, traffic regulation, and bandwidth reservation capabilities.H2020 project 5G-CLARITY 871428Spanish Government PID2019-108713RB-C53TRUE-5

Rendimiento de Redes 4G/5G usando una estación base real

Este artículo describe el desarrollo del proyecto sobre el despliegue de red móvil 5G y análisis de características de la misma. Actualmente, se encuentra en desarrollo y trata del análisis del rendimiento y comparación en redes 4G y 5G empleando una estación base real. En este trabajo se incluye la información sobre el estudio previo de estas redes, las alternativas de implementación, el despliegue realizado empleando el software de Amarisoft y testeo de capacidades de las redes propuestas.Este trabajo ha sido parcialmente financiado por el proyecto H2020 5G-CLARITY (Grant No. 871428) y el Ministerio de Economía y Competitividad (proyecto TEC2016-76795-C6-4-R)

Estudios actuales de literatura comparada. Teorías de la literatura y diálogos interdisciplinarios

Estos dos volúmenes constituyen una contribución al desarrollo de la comparatística que se realiza, principalmente, desde América Latina. El primer volumen está organizado en tres partes y consta de 22 artículos, mientras que el segundo reúne 24 capítulos.UCR::Vicerrectoría de Docencia::Artes y Letras::Facultad de Letras::Escuela de Filología, Lingüística y LiteraturaUCR::Vicerrectoría de Docencia::Ciencias Básicas::Sistema de Educación General::Escuela de Estudios GeneralesUCR::Vicerrectoría de Investigación::Sistema de Estudios de Posgrado::Artes y Letras::Maestría Académica en Literatura FrancesaUCR::Vicerrectoría de Investigación::Sistema de Estudios de Posgrado::Artes y Letras::Maestría Académica en Literatura LatinoamericanaUCR::Vicerrectoría de Docencia::Artes y Letras::Facultad de Letras::Escuela de Lenguas Moderna

Leveraging DRL for Traffic Prioritization in 5G and Beyond TSN-based Transport Networks

This paper has been presented in XXXVII Symposium of the International Union of Radio Science (URSI) 2022 celebrated in Malaga (Spain) from 5th to 7th September. This work includes a preliminary design and proof-of-concept to apply Reinforcement Learning for computing long-term configurations in asynchronous Time-Sensitive Networking (TSN)-based 5G and beyond transport networks. Most of this work has been carried out within the European 5G-CLARITY project (https://www.5gclarity.com/).Time-Sensitive Networking (TSN) is expected to become a key layer 2 technology for 5G and Beyond (5GB) transport networks (TN) as it allows for services with stringent and deterministic quality-of-service constraints and their coexistence with non-performance-sensitive traffic. Autonomous solutions for configuring TSN-based TNs are essential to ensure the deterministic QoS requisites of the 5GB streams while facilitating the zero-touch management of the network and reducing the operational costs. However, due to the configuration flexibility offered by TSN networks, using exact optimization methods to develop such solutions usually results in algorithms with high computational complexity. In this work, we propose and evaluate an initial design of a Reinforcement Learning (RL)-based solution for the long-term configuration of asynchronous TSN-based 5GB TNs. We successfully validated the proper operation of the proposal for an industrial private 5G scenario.H2020 research and innovation project 5G-CLARITY (Grant No. 871428)TRUE5G (PID2019- 108713RB-C53)6G-CHRONOS (TSI-063000-2021-28

Vídeos Semana de la Ciencia y la Tecnología 2011

Enlace a galería de vídeos Facultad de Ciencias: http://ciencias.uca.es/galeria/videos ; Enlace a YouTube: https://youtu.be/Lrq-1ZLr-ngVídeo de la actividad de divulgación científica Semana de la Ciencia y la Tecnología 2011, dirigida preferentemente a alumnos de 4º ESO y 1º de Bachillerato Científico-Tecnológico o Ciencias de la Salud, llevada a cabo en la Facultad de Ciencias de la Universidad de Cádiz, con la colaboración de la plataforma ES4FUN. Más información: http://hdl.handle.net/10498/17308

5G-CLARITY Deliverable D2.3 Primary System Architecture Evaluation

The present deliverable provides an initial evaluation of the key features of the 5G-CLARITY system architecture reported in [2] so that its main merits and limitations can be outlined. The activities carried out in this deliverable include: • Identification of components and features from the system architecture that will take part in the overall system evaluation. • The modelling of selected components and features, relying on theoretical analysis adopting both analytical and numerical models. • Definition of an evaluation plan, to specify the use case-based scenarios that will be used for the system architecture evaluation. For each scenario, this plan provides information of what the evaluation pursues and how it will be done, indicating: i) the selected components and features, together with their developed models; ii) the system level specification, by integrating individual models into end-to-end models that allows characterizing/profiling the scenario; and iii) the simulation and optimisation tools to be used for scenario evaluation. • System architecture evaluation execution, by validating the developed end-to-end models with the selected simulation and optimisation tools. This allows assessment of 5G-CLARITY system architecture through representative use cases, indicating clear benefits with respect to the relevant state-of-the-art as well as associated trade-offs. The outcomes from this first evaluation will be used to provide inputs to the work in WP3 and WP4, and to introduce necessary refinements in the final version of the 5G-CLARITY system architecture, to be published in the upcoming deliverable 5G-CLARITY D2.4

5G-PPP Technology Board:AI and ML – Enablers for Beyond 5G Networks

This white paper on AI and ML as enablers of beyond 5G (B5G) networks is based on contributions from 5G PPP projects that research, implement and validate 5G and B5G network systems. The white paper introduces the main relevant mechanisms in Artificial Intelligence (AI) and Machine Learning (ML), currently investigated and exploited for 5G and B5G networks. A family of neural networks is presented which are, generally speaking, non-linear statistical data modelling and decision-making tools. They are typically used to model complex relationships between input and output parameters of a system or to find patterns in data. Feed-forward neural networks, deep neural networks, recurrent neural networks, and convolutional neural networks belong to this family. Reinforcement learning is concerned about how intelligent agents must take actions in order to maximize a collective reward, e.g., to improve a property of the system. Deep reinforcement learning combines deep neural networks and has the benefit that is can operate on non-structured data. Hybrid solutions are presented such as combined analytical and machine learning modelling as well as expert knowledge aided machine learning. Finally, other specific methods are presented, such as generative adversarial networks and unsupervised learning and clustering. In the sequel the white paper elaborates on use case and optimisation problems that are being tackled with AI/ML, partitioned in three major areas namely, i) Network Planning, ii) Network Diagnostics/Insights, and iii) Network Optimisation and Control. In Network Planning, attention is given to AI/ML assisted approaches to guide planning solutions. As B5G networks become increasingly complex and multi-dimensional, parallel layers of connectivity are considered a trend towards disaggregated deployments in which a base station is distributed over a set of separate physical network elements which ends up in the growing number of services and network slices that need to be operated. This climbing complexity renders traditional approaches in network planning obsolete and calls for their replacement with automated methods that can use AI/ML to guide planning decisions. In this respect two solutions are discussed, first the network element placement problem is introduced which aims at improvements in the identification of optimum constellation of base stations each located to provide best network performance taking into account various parameters, e.g. coverage, user equipment (UE) density and mobility patterns (estimates), required hardware and cabling, and overall cost. The second problem considered in this regard is the dimensioning considerations for C-RAN clusters, in which employing ML-based algorithms to provide optimal allocation of baseband unit (BBU) functions (to the appropriate servers hosted by the central unit (CU)) to provide the expected gains is addressed. In Network Diagnostics, attention is given to the tools that can autonomously inspect the network state and trigger alarms when necessary. The contributions are divided into network characteristics forecasts solutions, precise user localizations methods, and security incident identification and forecast. The application of AI/ML methods in high-resolution synthesising and efficient forecasting of mobile traffic; QoE inference and QoS improvement by forecasting techniques; service level agreement (SLA) prediction in multi-tenant environments; and complex event recognition and forecasting are among network characteristics forecasts methods discussed. On high-precision user localization, AI-assisted sensor fusion and line-of-sight (LoS)/non-line-of-sight (NLoS) discrimination, and 5G localization based on soft information and sequential autoencoding are introduced. And finally, on forecasting security incidents, after a short introduction on modern attacks in mobile networks, ML-based network traffic inspection and real-time detection of distributed denial-of-service (DDoS) attacks are briefly examined. In regard to the Network Optimisation and Control, attention is given to the different network segments, including radio access, transport/fronthaul (FH)/backhaul (BH), virtualisation infrastructure, end-to-end 5G PPP Technology Board AI/ML for Networks 3 (E2E) network slicing, security, and application functions. Among application of AI/ML in radio access, the slicing in multi-tenant networks, radio resource provisioning and traffic steering, user association, demand-driven power allocation, joint MAC scheduling (across several gNBs), and propagation channel estimation and modelling are discussed. Moreover, these solutions are categorised (based on the application time-scale) into real-time, near-real-time, and non-real-time groups. On transport and FH/BH networks, AI/ML algorithms on triggering path computations, traffic management (using programmable switches), dynamic load balancing, efficient per-flow scheduling, and optimal FH/BH functional splitting are introduced. Moreover, federated learning across MEC and NFV orchestrators, resource allocation for service function chaining, and dynamic resource allocation in NFV infrastructure are among introduced AI/ML applications for virtualisation infrastructure. In the context of E2E slicing, several applications such as automated E2E service assurance, resource reservation (proactively in E2E slice) and resource allocation (jointly with slice-based demand prediction), slice isolation, and slice optimisation are presented. In regard to the network security, the application of AI/ML techniques in responding to the attack incidents are discussed for two cases, i.e. in moving target defence for network slice protection, and in self-protection against app-layer DDoS attacks. And finally, on the AI/ML applications in optimisation of application functions, the dash prefetching optimization and Q-learning applications in federated scenarios are presented.The white paper continues with the discussions on the application of AI/ML in the 5G and B5G network architectures. In this context the AI/ML based solutions pertaining to autonomous slice management, control and orchestration, cross-layer optimisation framework, anomaly detection, and management analytics, as well as aspects in AI/ML-as-a-service in network management and orchestration, and enablement of ML for the verticals' domain are presented. This is followed by topics on management of ML models and functions, namely the ML model lifecycle management, e.g., training, monitoring, evaluation, configuration and interface management of ML models. Furthermore, the white paper investigates the standardisation activities on the enablement of AI/ML in networks, including the definition of network data analytics function (NDAF) by 3GPP, the definition of an architecture that helps address challenges in network automation and optimization using AI and the categories of use cases where AI may benefit network operation and management by ETSI ENI, and finally the O-RAN definition of non-real-time and near-real-time RAN controllers to support ML-based management and intelligent RAN optimisation. Additionally, the white paper identifies the challenges in view of privacy and trust in AI/ML-based networks and potential solutions by introducing privacy preserving mechanisms and the zero-trust management approach are introduced. The availability of reliable data-sets as a crucial prerequisite to efficiency of AI/ML algorithms is discussed and the white paper concludes with a brief overview of AI/ML-based KPI validation and system troubleshooting. In summary the findings of this white paper conclude with the identification of several areas (research and development work) for further attention in order to enhance future network return-on-investment (ROI): (a) building standardized interfaces to access relevant and actionable data, (b) exploring ways of using AI to optimize customer experience, (c) running early trials with new customer segments to identify AI opportunities, (d) examining use of AI and automation for network operations, including planning and optimization, (e) ensuring early adoption of new solutions for AI and automation to facilitate introduction of new use cases, and (f) establish/launch an open repository for network data-sets that can be used for training and benchmarking algorithms by all