Academic literature on the topic 'Network slicing in 5G'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Network slicing in 5G.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Network slicing in 5G"
1
Gao, Shujuan, Ruyan Lin, Yulong Fu, Hui Li, and Jin Cao. "Security Threats, Requirements and Recommendations on Creating 5G Network Slicing System: A Survey." Electronics 13, no. 10 (2024): 1860. http://dx.doi.org/10.3390/electronics13101860.
Full textAbstract:
Network slicing empowers 5G with enhanced network performance and efficiency, cost saving, and better QoS and customer satisfaction, and expands the commercial application scenarios of 5G networks. However, the introduction of new techniques usually raises new security threats. Most of the existing works on 5G security only focus on 5G itself and do not analyze 5G network slicing security in detail. We consider network slices as a virtual logical network that can unite the subnetwork parts of 5G. If a 5G network slice has security problems or has been attacked, the entire 5G network will have security risks. In this paper, after synthesizing the existing literature, we analyze the security threats step by step through the lifecycle of 5G network slices, analyzing and summarizing more than 70 security threats in three major categories. Based on the security issues investigated, from a viewpoint of building a secure 5G network slicing system, we compiled 24 security requirements and proposed the corresponding recommendations for different scenarios of 5G network slicing. Finally, we collated the future research trends of 5G network slicing security.
2
Makhija, Deven. "5G Communication Systems: Network Slicing and Virtual Private Network Architecture." ITM Web of Conferences 54 (2023): 02001. http://dx.doi.org/10.1051/itmconf/20235402001.
Full textAbstract:
5G communication systems are being rolled out with multiple technological solutions and applications being fielded on existing as well as enhanced infrastructure. The utilization of 5G systems and infrastructure by verticals over different platforms as well as industries is achieved with slicing. Slicing in 5G provides guaranteed resources for end users of vertical industries and applications over varied platforms, architecture, and infrastructure. Standards for network slicing in 5G have been formulated by 3GPP and further specifications are being released. Implementation of slicing at various layers are being researched. This Paper reviews the advancements in development of specifications for Layer 2 implementation of slicing and communication systems using virtual Private Network and Virtual Transport Network and its architecture. The enhancements to communication systems using existing Multi-Protocol Label Switching (MPLS) and its exploitation based on slicing technology has been reviewed. The research challenges and way ahead on same have been discussed including end resource allocation.
3
Munir, Rizwan, Yifei Wei, Chao Ma, and Bizhu Yang. "Dynamically Resource Allocation in Beyond 5G (B5G) Network RAN Slicing Using Deep Deterministic Policy Gradient." Wireless Communications and Mobile Computing 2022 (December 21, 2022): 1–13. http://dx.doi.org/10.1155/2022/9958786.
Full textAbstract:
Network slicing makes it possible for future applications with a variety of adaptability requirements and performance requirements by spliting the physical network into several logical networks. Radio access network (RAN) slicing’s main goal is to assign physical resource blocks (RBs) to mMTC, eMBB, and uRLLC services while ensuring the Quality of service (QoS). Consequently, it is challenging to determine the optimal strategies for 5G radio access network (5G-RAN) slicing because of dynamically changes in slice needs and environmental data, and conventional approaches have difficulty addressing resource allocation issues. In this paper, we present an energy-efficient deep deterministic policy gradient resource allocation (EE-DDPG-RA) method for RAN slicing in 5G networks to choose the resource allocation policy that increases long-term throughput while satisfying the requirements of B5G systems for quality of service. This method’s main goal is to remove unnecessary actions in order to lower the amount of available action space. The numerical outcomes demonstrate that the proposed approach outperforms boundaries by enhancing deep-rooted throughput and effectively managing resources.
4
Jia, Qingmin, RenChao Xie, Tao Huang, Jiang Liu, and Yunjie Liu. "Caching Resource Sharing for Network Slicing in 5G Core Network." Journal of Organizational and End User Computing 31, no. 4 (2019): 1–18. http://dx.doi.org/10.4018/joeuc.2019100101.
Full textAbstract:
Network slicing has been considered a promising technology in next generation mobile networks (5G), which can create virtual networks and provide customized service on demand. Most existing works on network slicing mainly focus on virtualization technology, and have not considered in-network caching well. However, in-network caching, as the one of the key technologies for information-centric networking (ICN), has been considered as a significant approach in 5G network to cope with the traffic explosion and network challenges. In this article, the authors jointly consider in-network caching combining with network slicing. They propose an efficient caching resource sharing scheme for network slicing in 5G core network, aiming at solving the problem of how to efficiently share the limited physical caching resource of Infrastructure Provider (InP) among multiple network slices. In addition, from the perspective of network slicing, the authors formulate caching resource sharing problem as a non-cooperative game, and propose an iteration algorithm based on caching resource updating to obtain the Nash Equilibrium solution. Simulation results show that the proposed algorithm has good convergence performance, and illustrate the effectiveness of the proposed scheme.
5
Wong, Stan, Bin Han, and Hans D. Schotten. "5G Network Slice Isolation." Network 2, no. 1 (2022): 153–67. http://dx.doi.org/10.3390/network2010011.
Full textAbstract:
This article reveals an adequate comprehension of basic defense, security challenges, and attack vectors in deploying multi-network slicing. Network slicing is a revolutionary concept of providing mobile network on-demand and expanding mobile networking business and services to a new era. The new business paradigm and service opportunities are encouraging vertical industries to join and develop their own mobile network capabilities for enhanced performances that are coherent with their applications. However, a number of security concerns are also raised in this new era. In this article, we focus on the deployment of multi-network slicing with multi-tenancy. We identify the security concerns and discuss the defense approaches such as network slice isolation and insulation in a multi-layer network slicing security model. Furthermore, we identify the importance to appropriately select the network slice isolation points and propose a generic framework to optimize the isolation policy regarding the implementation cost while guaranteeing the security and performance requirements.
6
Dayot, Ralph Voltaire J., In-Ho Ra, and Hyung-Jin Kim. "A Deep Contextual Bandit-Based End-to-End Slice Provisioning Approach for Efficient Allocation of 5G Network Resources." Network 2, no. 3 (2022): 370–88. http://dx.doi.org/10.3390/network2030023.
Full textAbstract:
5G networks have been experiencing challenges in handling the heterogeneity and influx of user requests brought upon by the constant emergence of various services. As such, network slicing is considered one of the critical technologies for improving the performance of 5G networks. This technology has shown great potential for enhancing network scalability and dynamic service provisioning through the effective allocation of network resources. This paper presents a Deep Reinforcement Learning-based network slicing scheme to improve resource allocation in 5G networks. First, a Contextual Bandit model for the network slicing process is created, and then a Deep Reinforcement Learning-based network slicing agent (NSA) is developed. The agent’s goal is to maximize every action’s reward by considering the current network state and resource utilization. Additionally, we utilize network theory concepts and methods for node selection, ranking, and mapping. Extensive simulation has been performed to show that the proposed scheme can achieve higher action rewards, resource efficiency, and network throughput compared to other algorithms.
7
MAKSYMYUK, Taras, Volodymyr ANDRUSHCHAK, Stepan DUMYCH, Bohdan SHUBYN, Gabriel BUGÁR, and Juraj GAZDA. "BLOCKCHAIN-BASED NETWORK FUNCTIONS VIRTUALIZATION FOR 5G NETWORK SLICING." Acta Electrotechnica et Informatica 20, no. 4 (2021): 54–59. http://dx.doi.org/10.15546/aeei-2020-0026.
Full textAbstract:
The paper proposes a new blockchain-based network architecture for 5G network functions virtualization. By using a combination of AI and blockchain technologies, proposed system provides flexible network deployment, interoperability between different mobile network operators and effective management of radio resources. Experimental testbed of the proposed system has been implemented by using cloud and edge computing infrastructure and software defined radio peripheral NI USRP 2900. Simulation results of the network slicing and radio resource management shows that proposed system is able to double the capacity of the physical network infrastructure, while ensuring the target quality of service for all users.
8
Kannan, Yamini. "Network Slicing in 5G Systems: Challenges, Opportunities and Implementation Approaches." International Journal of Innovative Research in Information Security 10, no. 02 (2024): 51–56. http://dx.doi.org/10.26562/ijiris.2023.v1002.02.
Full textAbstract:
The introduction of the fifth-generation (5G) of network technology has radically transformed the telecommunications landscape by providing high-speed, low-latency communication suitable for a range of innovative applications. However, this transformation also introduces novel network complexity and resource management challenges. An emerging solution to these formidable challenges is 'Network Slicing,' a powerful technology that plays a crux role in the efficient management of 5G network systems. Network slicing, enabled by key technologies such as Software-Defined Networking (SDN) and Network Function Virtualization (NFV), allows the creation of multiple virtual and independent networks operating on a shared physical infrastructure. This ability contributes to a more flexible, scalable, and efficient network system, making it aptly suited for diverse 5G applications. In this paper, we conduct an in-depth examination of network slicing in 5G systems, its implementation strategies, associated challenges, and potential solutions. Two real-world case studies underline its practical applications, while a discussion on the future outlook anticipates advances in AI and ML to refine network slicing management. The paper posits that while network slicing brings its own set of complexities, its continuous evolution and relentless innovations gear towards overcoming such challenges, paving the way to a future of 5G networking marked by versatility, reliability, and efficiency of unprecedented levels.
9
Dangi, Ramraj, Akshay Jadhav, Gaurav Choudhary, Nicola Dragoni, Manas Kumar Mishra, and Praveen Lalwani. "ML-Based 5G Network Slicing Security: A Comprehensive Survey." Future Internet 14, no. 4 (2022): 116. http://dx.doi.org/10.3390/fi14040116.
Full textAbstract:
Fifth-generation networks efficiently support and fulfill the demands of mobile broadband and communication services. There has been a continuing advancement from 4G to 5G networks, with 5G mainly providing the three services of enhanced mobile broadband (eMBB), massive machine type communication (eMTC), and ultra-reliable low-latency services (URLLC). Since it is difficult to provide all of these services on a physical network, the 5G network is partitioned into multiple virtual networks called “slices”. These slices customize these unique services and enable the network to be reliable and fulfill the needs of its users. This phenomenon is called network slicing. Security is a critical concern in network slicing as adversaries have evolved to become more competent and often employ new attack strategies. This study focused on the security issues that arise during the network slice lifecycle. Machine learning and deep learning algorithm solutions were applied in the planning and design, construction and deployment, monitoring, fault detection, and security phases of the slices. This paper outlines the 5G network slicing concept, its layers and architectural framework, and the prevention of attacks, threats, and issues that represent how network slicing influences the 5G network. This paper also provides a comparison of existing surveys and maps out taxonomies to illustrate various machine learning solutions for different application parameters and network functions, along with significant contributions to the field.
10
Sohaib, Rana Muhammad, Oluwakayode Onireti, Yusuf Sambo, and Muhammad Ali Imran. "Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case." Electronics 10, no. 9 (2021): 1090. http://dx.doi.org/10.3390/electronics10091090.
Full textAbstract:
As the idea of a new wireless communication standard (5G) started to circulate around the world, there was much speculation regarding its performance, making it necessary to carry out further research by keeping in view the challenges presented by it. 5G is considered a multi-system support network due to its ability to provide benefits to vertical industries. Due to the wide range of devices and applications, it is essential to provide support for massively interconnected devices. Network slicing has emerged as the key technology to meet the requirements of the communications network. In this paper, we present a review of the latest achievements of 5G network slicing by comparing the architecture of The Next Generation Mobile Network Alliance’s (NGMN’s) and 5G-PPP, using the enabling technologies software-defined networking (SDN) and network function virtualization (NFV). We then review and discuss machine learning (ML) techniques and their integration with network slicing for beyond 5G networks and elaborate on how ML techniques can be useful for mobility prediction and resource management. Lastly, we propose the use case of network slicing based on ML techniques in a smart seaport environment, which will help to manage the resources more efficiently.
Dissertations / Theses on the topic "Network slicing in 5G"
1
Bakri, Sihem. "Towards enforcing network slicing in 5G networks." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS067.
Full textAbstract:
Les architectures de réseaux sans fil actuelles, de type « une taille pour tous », ne peuvent pas prendre en charge ces critères de services hétérogènes de nouvelle génération 5G. Par conséquent, la recherche autour de la 5G vise à fournir des architectures et des mécanismes plus adéquats pour répondre à ce besoin. L'architecture 5G est conçue pour répondre aux exigences variées et contradictoires des services, en termes de latence, de bande passante et de fiabilité, qui ne peuvent être assurées par la même infrastructure du réseau. Dans ce contexte, le découpage du réseau fourni par la virtualisation du réseau permet de diviser l'infrastructure en différentes tranches, chaque tranche est adaptée aux besoins spécifiques des services, où elle permet à différents services (comme l'automobile, l'Internet des objets...) d'être fournis par différentes instances de la tranche du réseau. Les chercheurs ont défini trois grandes classes de services de découpage en réseau, qui sont: enhanced Mobile BroadBand (eMBB), massive Machine Type Communication (mMTC), and ultra-Reliable and Low-Latency Communication (uRLLC). L'un des principaux défis du déploiement des tranches de réseau est le découpage du réseau d'accès radio (RAN). En effet, la gestion des ressources RAN et leur partage entre les tranches de réseau est une tâche particulièrement difficile. Cette thèse propose des solutions qui visent à améliorer les performances du réseau et d'introduire de la flexibilité et une plus grande utilisation des ressources du réseau, en fournissant de manière précise et dynamique aux tranches de réseau activées les quantités de ressources appropriées pour répondre à leurs divers besoins<br>The current architecture “one size fits all” of 4G network cannot support the next-generation 5G heterogeneous services criteria. Therefore, research around 5G aims to provide more adequate architectures and mechanisms to deal with this purpose. The 5G architecture is envisioned to accommodate the diverse and conflicting demands of services in terms of latency, bandwidth, and reliability, which cannot be sustained by the same network infrastructure. In this context, network slicing provided by network virtualization allows the infrastructure to be divided into different slices. Each slice is tailored to meet specific service requirements allowing different services (such as automotive, Internet of Things, etc.) to be provided by different network slice instances. Each of these instances consists of a set of virtual network functions that run on the same infrastructure with specially adapted orchestration. Three main service classes of network slicing have been defined by the researchers as follows: Enhanced Mobile Broadband (eMBB), massive Machine Type Communication (mMTC), and ultra-Reliable and Low-Latency Communication (uRLLC). One of the main challenges when it comes to deploying Network Slices is slicing the Radio Access Network (RAN). Indeed, managing RAN resources and sharing them among Network Slices is an increasingly difficult task, which needs to be properly designed. This thesis proposes solutions that aim to improve network performance, and introduce flexibility and greater utilization of network resources by accurately and dynamically provisioning the activated network slices with the appropriate amounts of resources to meet their diverse requirements
2
Suárez, Trujillo Luis Carlos. "Securing network slices in 5th generation mobile networks." Thesis, Brest, 2020. http://www.theses.fr/2020BRES0050.
Full textAbstract:
Le « network slicing » est la pierre angulaire pour la conception et le déploiement de services de communication à forte valeur ajoutée qui seront supportés par les nouveaux cas d’usage introduits par la nouvelle architecture 5G. Ce document souligne le défi que représente l’isolation des « network slices », et la gestion de sa sécurité en fonction des politiques retenues.Tout d’abord, un nouveau modèle de contrôle d’accès a été créé. Il permet de sécuriser les interactions entre les fonctions réseaux supportées par les systèmes 5G. Ensuite, la gestion des interactions entre les «network slices » a été abordée. On utilise le concept de chaînes de « network slices », qui seront mises en oeuvre après validation des contraintes de sécurité selon la politique choisie. Enfin, une méthode de quantification de l’isolation a été mise au point, permettant de connaître le degré d’isolation d’un service de communication offert via des « network slices». Cela permet aux opérateurs de réseau et aux clients de mesurer le degré d’isolation, puis d’améliorer la configuration des « network slices » afin de le renforcer. Ces éléments établissent un cadre solide contribuant à sécuriser, verticalement, les services de communication d’un réseau 5G et à évaluer le degré de sécurité en ce qui concerne leurs interactions et leur isolation<br>Network slicing is a cornerstone in the conception and deployment of enriched communication services for the new use cases envisioned and supported by the new 5G architecture.This document makes emphasis on the challenge of the network slicing isolation and security management according to policy. First, a novel access control model was created, that secures the interactions between network functions that reside inside the 5G system. Then, the management of the interactions between network slices was addressed. We coin the concept of network slice chains, which are conceived after security constraint validation according to policy. Lastly, a method to quantify isolation was developed, permitting to find out how well isolated a communication service is, which is offered via network slices. This enables network operators and customers to measure the isolation level and improve the configuration of the network slices so the isolation level can be enhanced. These components establish a solid framework that contributes to secure, vertically, the communication services of a 5G network and assess how secure they are with respect to their interactions and isolation
3
Chang, Chia-Yu. "Cloudification and Slicing in 5G Radio Access Network". Electronic Thesis or Diss., Sorbonne université, 2018. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2018SORUS293.pdf.
Full textAbstract:
Au cours des dernières décennies, la croissance des statistiques d’utilisation de réseau exige une technologique évolutive. Une question naturelle surgit dans nos esprits: que sera la 5G? Pour répondre à cette question, l’architecture 5G doit être conçue avec un certain niveau de flexibilité via l’intégration des principes de softwarization et virtualisation. Le réseau peut être utilisé de manière efficace et indépendante via la création de plusieurs espaces séparées logiquement, appelés tranches de réseau. De plus, chaque réseau logique peut déployer ses fonctions de réseau dans un environnement de nuage avec la flexibilité d’exécution. À cette fin, l’objectif de cette thèse est d’étudier ces deux techniques: (a) C-RAN et (b) découpage de RAN. Dans la première partie, nous étudions C-RAN, dans lequel les stations de base monolithiques sont remplacées par (1) les éléments radio distribués et (2) les pools centralisés pour des unités de traitement en bande de base. Le concept C-RAN est toujours confronté à des exigences sévères en matière de capacité et de latence de l’interface fronthaul qui connecte l’unité de radio distante distribuée à l’unité de traitement en bande de base centralisée. Dans la deuxième partie, nous nous concentrons sur le découpage RAN non seulement pour permettre des différents niveaux d’isolation et de partage à chaque tranche de réseau, mais également pour customiser le plan de contrôle, le plan utilisateur et la logique de contrôle de réseau virtualisé. Par conséquent, nous proposons un environnement d’exécution flexible pour le système de slicing, nommé «RAN Runtime» pour héberger les instances de service sur chacun des modules RAN sous-jacents<br>Over the past few decades, the continuing growth of network statistics requires a constantly evolving technology. Therefore, a natural question arises in our minds: what will 5G be? To answer this question, the 5G architecture must be designed with a certain level of flexibility through the integration of softwarization and virtualization principles. Therefore, we can see that 5G will provide a paradigm shift beyond radio access technology in order to establish an agile and sophisticated communication system. The network can be used efficiently and independently by creating multiple logically separated spaces, called network slices. In addition, each logical network can deploy its network functions in a flexible cloud environment. To this end, the goal of this thesis is to study these two techniques: (a) Cloud-RAN and (b) RAN splitting. In the first part, our focus is on the C-RAN concept, in which monolithic base stations are replaced by (1) distributed radio elements and (2) centralized pools for baseband processing units. The C-RAN notion is still confronted with stringent capacity and latency requirements of the fronthaul interface that connects the distributed remote radio unit to the centralized baseband processing unit. In the second part, we focus on RAN cutting not only to allow different levels of isolation and sharing at each slice of network, but also to customize the control plane, user plane and control logic. Therefore, we provide a flexible runtime environment for the "RAN Runtime" slicing system to host service instances on each of the underlying RAN modules
4
Biallach, Hanane. "Optimization of VNF reconfiguration problem for 5G network slicing." Electronic Thesis or Diss., Compiègne, 2022. http://www.theses.fr/2022COMP2707.
Full textAbstract:
Ces dernières années, en raison de la croissance sans précédent du nombre d'appareils connectés et de données mobiles, et des développements continus des technologies pour répondre à cette énorme demande de données, le réseau de cinquième génération (5G) a émergé. La future architecture 5G sera essentiellement basée sur le Network Slicing (NS), qui permet de fournir une approche flexible pour réaliser la vision 5G. Grâce au concept émergent de virtualisation des fonctions réseau (NFV), les fonctions réseau sont découplées des matériels physiques dédiés et réalisées sous forme de logiciel. Cela offre plus de flexibilité et d'agilité dans les opérations commerciales. Malgré les avantages qu'il apporte, NFV soulève quelques défis techniques, le problème de reconfiguration étant l'un d'entre eux. Ce problème, qui est NP-difficile, consiste à réaffecter les fonctions de réseau virtuel (VNFs) pour s'adapter aux changements du réseau, en transformant l'état courant des services déployés, on peut illustrer cela par la migration des machines virtuelles (VM) qui hébergent les VNF, à un autre état qui répond aux objectives des opérateurs. Cette thèse de doctorat étudie comment reconfigurer les VNFs en les migrant vers un état optimal qui pourrait être calculé en avance ou inconnu. Dans cette thèse, nous avons étudié les deux cas en minimisant la durée d'interruption de service et la durée de migration des VNFs. Nous avons proposé des méthodes exactes et approchées. Parmi les méthodes exactes, nous citons deux modèles PLNE. Nous avons également proposé deux approches heuristiques, l'une basée sur la génération de colonnes et la deuxième utilisant la notion de “feedback arc set". L'objectif global de ce travail est donc de définir et d'étudier le problème de reconfiguration des VNFs dans le contexte du 5G network slicing, et de proposer des modèles mathématiques et des algorithmes efficaces pour résoudre les problèmes d'optimisation sous-jacents<br>In recent years, because of the unprecedented growth in the number of connected devices and mobile data, and the ongoing developments in technologies to address this enormous data demand, the fifth generation (5G) network has emerged. The forthcoming 5G architecture will be essentially based on Network Slicing (NS), which enables provide a flexible approach to realize the 5G vision. Thanks to the emerging Network Function Virtualization (NFV) concept, the network functions are decoupled from dedicated hardware devices and realized in the form of software. This offers more flexibility and agility in business operations. Despite the advantages it brings, NFV raises some technical challenges, the reconfiguration problem is one of them. This problem, which is NP-Hard, consists in reallocating the Virtual Network Functions (VNFs) to fit the network changes, by transforming the current state of deployed services, e.g., the current placement of Virtual Machines (VM) that host VNFs, to another state that updates providers’ objectives. This PhD thesis investigates how to reconfigure the VNFs by migrating them to an optimal state that could be computed in advance or free placement. In this thesis, we studied both cases while minimizing the service interruption duration and the VNF migration duration. We have proposed exact and approximate methods. Among the exact methods, we cite two ILP models. We also proposed two heuristic approaches, one based on column generation and the second using the concept of “arc set feedback”. The overall objective of this work is therefore to define and study the problem of VNF reconfiguration problem in the context of 5G network slicing, and propose mathematical models and efficient algorithms to solve the underlying optimization problems
5
Motyčka, Jan. "Implementace mechanismů zajišťujících “RAN Slicing” v simulačním nástroji Network Simulator 3." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442360.
Full textAbstract:
This thesis deals with the topic of network slicing technology in 5G networks, mainly on the RAN part. In the theoretical part, basic principles of 5G network slicing in core network part and RAN part are presented. Practical part contains a simulation scenario created in NS3 simulator with LENA 5G module. Results of this simulation are presented and discussed with the emphasis on RAN slicing.
6
Sid-Otmane, Jonathan. "A study of data consistency constraints in 5G, applied to limiting resource usage in network slices." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS537.
Full textAbstract:
La spécification du réseau 5G requiert des propriétés ACID (Atomicity, Consistency, Isolation, Durability) pour sa base de données distribuée. D’après le théorème de CAP, ce niveau de cohérence est incompatible avec un système géo-distribué de haute disponibilité. Les réseaux mobiles étant sujets à des obligations de disponibilité, cette contradiction remet en question la spécification. Notre thèse se concentre sur une étude de l’usage des données dans le réseau qui nous permet d’en extraire des propriétés de cohérence plus faibles que ACID mais suffisantes pour que ces données restent correctes. Dans le cas du Network Slicing, une innovation propre aux réseaux 5G, nous proposons une étude plus approfondie de l’application d’une de ces propriétés, à travers l’exemple d’une limite qui borne globalement l’utilisation des ressources du réseau pour un slice. Les données liées à une slice sont potentiellement largement distribuées, et l’application d’une limite y est donc plus difficile. Nous proposons plusieurs algorithmes placés à différents niveaux de cohérence afin d’étudier leur performance dans une simulation du réseau 5G<br>The 5G network specification requires ACID properties (Atomicity, Consistency, Isolation, Durability) for its distributed database. According to the CAP theorem, these properties are incompatible with high availability for a geo-distributed system. Since availability is an obligation for mobile networks, this contradiction challenges the specification. Our thesis focus on a study of the usage of data in the network that allow us to extract weaker consistency properties that are still sufficient to maintain the correctness of the data. In the case a Network Slicing, an innovation of the 5G network, we propose to study further the enforcement of one of these properties, through the example of the enforcement of a global limit on the resource usage of a slice. Slices are deployed over a potentially large are, and so is their data, which make the enforcement of a limit more challenging. We propose several algorithms placed at different points of the consistency spectrum and study their performance in a simulation of the 5G network
7
Matoussi, Salma. "User-Centric Slicing with Functional Splits in 5G Cloud-RAN." Electronic Thesis or Diss., Sorbonne université, 2021. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2021SORUS004.pdf.
Full textAbstract:
Le réseau d’accès radio (RAN) 5G vise à faire évoluer de nouvelles technologies couvrant l’infrastructure Cloud, les techniques de virtualisation et le réseau défini par logiciel (SDN). Des solutions avancées sont introduites pour répartir les fonctions du réseau d’accès radio entre des emplacements centralisés et distribués (découpage fonctionnel) afin d’améliorer la flexibilité du RAN. Cependant, l’une des préoccupations majeures est d’allouer efficacement les ressources RAN, tout en prenant en compte les exigences hétérogènes des services 5G. Dans cette thèse, nous abordons la problématique du provisionnement des ressources Cloud RAN centré sur l’utilisateur (appelé tranche d’utilisateurs ). Nous adoptons un déploiement flexible du découpage fonctionnel. Notre recherche vise à répondre conjointement aux besoins des utilisateurs finaux, tout en minimisant le coût de déploiement. Pour surmonter la grande complexité impliquée, nous proposons d’abord une nouvelle implémentation d’une architecture Cloud RAN, permettant le déploiement à la demande des ressources, désignée par AgilRAN. Deuxièmement, nous considérons le sous-problème de placement des fonctions de réseau et proposons une nouvelle stratégie de sélection de découpage fonctionnel centrée sur l’utilisateur nommée SPLIT-HPSO. Troisièmement, nous intégrons l’allocation des ressources radio. Pour ce faire, nous proposons une nouvelle heuristique appelée E2E-USA. Dans la quatrième étape, nous envisageons une approche basée sur l’apprentissage en profondeur pour proposer un schéma d’allocation temps réel des tranches d’utilisateurs, appelé DL-USA. Les résultats obtenus prouvent l’efficacité de nos stratégies proposées<br>5G Radio Access Network (RAN) aims to evolve new technologies spanning the Cloud infrastructure, virtualization techniques and Software Defined Network capabilities. Advanced solutions are introduced to split the RAN functions between centralized and distributed locations to improve the RAN flexibility. However, one of the major concerns is to efficiently allocate RAN resources, while supporting heterogeneous 5G service requirements. In this thesis, we address the problematic of the user-centric RAN slice provisioning, within a Cloud RAN infrastructure enabling flexible functional splits. Our research aims to jointly meet the end users’ requirements, while minimizing the deployment cost. The problem is NP-hard. To overcome the great complexity involved, we propose a number of heuristic provisioning strategies and we tackle the problem on four stages. First, we propose a new implementation of a cost efficient C-RAN architecture, enabling on-demand deployment of RAN resources, denoted by AgilRAN. Second, we consider the network function placement sub-problem and propound a new scalable user-centric functional split selection strategy named SPLIT-HPSO. Third, we integrate the radio resource allocation scheme in the functional split selection optimization approach. To do so, we propose a new heuristic based on Swarm Particle Optimization and Dijkstra approaches, so called E2E-USA. In the fourth stage, we consider a deep learning based approach for user-centric RAN Slice Allocation scheme, so called DL-USA, to operate in real-time. The results obtained prove the efficiency of our proposed strategies
8
Maiorano, Picone Pasquale Carlo. "A QoS Controller Framework Compliant with the ETSI Network Function Virtualization Specification." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/10406/.
Full textAbstract:
The 5th generation of mobile networking introduces the concept of “Network slicing”, the network will be “sliced” horizontally, each slice will be compliant with different requirements in terms of network parameters such as bandwidth, latency. This technology is built on logical instead of physical resources, relies on virtual network as main concept to retrieve a logical resource. The Network Function Virtualisation provides the concept of logical resources for a virtual network function, enabling the concept virtual network; it relies on the Software Defined Networking as main technology to realize the virtual network as resource, it also define the concept of virtual network infrastructure with all components needed to enable the network slicing requirements. SDN itself uses cloud computing technology to realize the virtual network infrastructure, NFV uses also the virtual computing resources to enable the deployment of virtual network function instead of having custom hardware and software for each network function.
The key of network slicing is the differentiation of slice in terms of Quality of Services parameters, which relies on the possibility to enable QoS management in cloud computing environment.
The QoS in cloud computing denotes level of performances, reliability and
availability offered. QoS is fundamental for cloud users, who expect providers to deliver the advertised quality characteristics, and for cloud providers, who need to find the right tradeoff between QoS levels that has possible to offer and operational costs.
While QoS properties has received constant attention before the advent of cloud computing, performance heterogeneity and resource isolation mechanisms of cloud platforms have significantly complicated QoS analysis and deploying, prediction, and assurance. This is prompting several researchers to investigate automated QoS management methods that can leverage the high programmability of hardware and software resources in the cloud.
9
Boutiba, Karim. "On enforcing Network Slicing in the new generation of Radio Access Networks." Electronic Thesis or Diss., Sorbonne université, 2024. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2024SORUS003.pdf.
Full textAbstract:
Les réseaux 5G émergents et au-delà promettent de prendre en charge de nouveaux cas d'utilisation tels que la communication holographique immersive, l'internet des compétences et la cartographie interactive 4D [1]. Ces cas d'usage ont des exigences strictes en termes de Quality de Service (Quality of Service), telles qu'une faible latence, un débit descendant et ascendant (Downlink (DL)/Uplink (UL)) élevé, ainsi qu'une faible consommation d'énergie. Les spécifications du groupe de normalisation 3GPP ont introduit de nombreuses fonctionnalités aux système radio 5G (5G NR), dans le but d'améliorer l'efficacité spectrale de la 5G et de répondre aux exigences strictes et hétérogènes des services de la 5G et au-delà. Parmi les principales fonctionnalités de la 5G NR, on peut citer l'introduction du concept de numérologie et BandWidth Part (BWP), le multiplexage temporel (TDD) dynamique et Connected-mode Discontinuous Reception (C-DRX). Toutefois, les spécifications 3GPP n'indiquent pas comment configurer la next gNode B (gNB)/User Equipment (UE) pour optimiser l'utilisation des fonctionnalités 5G NR. Afin de combler ce manque, nous proposons de nouvelles solutions qui mettent en œuvre des fonctionnalités 5G NR en appliquant les techniques de l'apprentissage automatique ou Machine Learning (ML), en particulier l'apprentissage profond par renforcement ou Deep Reinforcement Learning (DRL). En effet, les outils de l'intelligence artificielle jouent un rôle essentiel dans l'optimisation des systèmes de communication et des réseaux [2] grâce à leurs capacités à rendre le réseau capable de s'auto-configurer et s'auto-optimiser.Dans cette thèse, nous proposons plusieurs solutions pour permettre une configuration intelligente du réseau d'accès radio (RAN). Nous avons divisé les solutions en trois parties distinctes.Dans la première partie, nous proposons deux contributions. Tout d'abord, nous présentons NRflex, une solution de découpage du RAN en tranches (ou slicing), aligné sur l'architecture Open RAN (O-RAN). Par la suite, nous modélisons le problème de découpage du RAN en tranches comme un problème Mixed-Integer Linear Programming (MILP). Après avoir montré que la résolution du problème prend un temps exponentiel, nous avons introduit une nouvelle approche pour le résoudre en un temps polynomial, ce qui est très important pour la fonction de l'ordonnancement (scheduling) des ressources radio. La nouvelle approche consiste à formaliser et résoudre ce problème par le biais l'apprentissage par renforcement profond (DRL).Dans la deuxième partie de la thèse, nous proposons une solution basée sur le DRL pour permettre un TDD dynamique dans une seule cellule 5G NR. La solution a été implémentée dans la plateforme OpenAirInterface (OAI) et testée avec UEs réels. Nous avons ensuite étendu la solution, en tirant parti de Multi-Agent Deep Reinforcement Learning (MADRL), pour prendre en charge plusieurs cellules en tenant compte de l'interférence radio entre les liaisons transversales entre les cellules.Dans la dernière partie de la thèse, nous avons proposé trois solutions pour optimiser le RAN afin de prendre en charge les services URLLC. Tout d'abord, nous avons proposé une solution en deux étapes basées sur l'apprentissage automatique pour prédire les coupures du lien radio ou Radio Link Failure (RLF). Le modèle de prédiction RLF a été entraîné avec des données réelles obtenues à partir d'un banc d'essai 5G. Dans la deuxième contribution, nous avons proposé une solution basée sur le DRL pour réduire la latence UL. Notre solution alloue (prédit) dynamiquement les futurs besoins en ressource radio du UL en apprenant du modèle de trafic. Dans la dernière contribution, nous introduisons une solution basée sur le DRL afin d'équilibrer la latence et la consommation d'énergie en calculant conjointement les paramètres C-DRX et la configuration BWP<br>The emerging 5G networks and beyond promise to support novel use cases such as immersive holographic communication, Internet of Skills, and 4D Interactive mapping [usecases]. These use cases require stringent requirements in terms of Quality of Service (QoS), such as low latency, high Downlink (DL)/Uplink (UL) throughput and low energy consumption. The 3rd Generation Partnership Project (3GPP) specifications introduced many features in 5G New Radio (NR) to improve the physical efficiency of 5G to meet the stringent and heterogeneous requirements of beyond 5G services. Among the key 5G NR features, we can mention the numerology, BandWidth Part (BWP), dynamic Time Duplex Division (TDD) and Connected-mode Discontinuous Reception (C-DRX). However, the specifications do not provide how to configure the next Generation Node B (gNB)/User Equipment (UE) in order to optimize the usage of the 5G NR features. We enforce the 5G NR features by applying Machine Learning (ML), particularly Deep Reinforcement Learning (DRL), to fill this gap. Indeed, Artificial Intelligence (AI)/ML is playing a vital role in communications and networking [1] thanks to its ability to provide a self-configuring and self-optimizing network.In this thesis, different solutions are proposed to enable intelligent configuration of the Radio Access Network (RAN). We divided the solutions into three different parts. The first part concerns RAN slicing leveraging numerology and BWPs. In contrast, the second part tackles dynamic TDD, and the last part goes through different RAN optimizations to support Ultra-Reliable and Low-Latency Communication (URLLC) services.In the first part, we propose two contributions. First, we introduce NRflex, a RAN slicing framework aligned with Open RAN (O-RAN) architecture. NRflex dynamically assigns BWPs to the running slices and their associated User Equipment (UE) to fulfill the slices' required QoS. Then, we model the RAN slicing problem as a Mixed-Integer Linear Programming (MILP) problem. To our best knowledge, this is the first MILP modeling of the radio resource management featuring network slicing, taking into account (i) Mixed-numerology, (ii) both latency and throughput requirements (iii) multiple slices attach per UE (iv) Inter-Numerology Interference (INI). After showing that solving the problem takes an exponential time, we consider a new approach in a polynomial time, which is highly required when scheduling radio resources. The new approach consists of formalizing this problem using a DRL-based solver.In the second part of this thesis, we propose a DRL-based solution to enable dynamic TDD in a single 5G NR cell. The solution is implemented in OAI and tested using real UEs. Then, we extend the solution by leveraging Multi-Agent Deep Reinforcement Learning (MADRL) to support multiple cells, considering cross-link interference between cells.In the last part, we propose three solutions to optimize the RAN to support URLLC services. First, we propose a two-step ML-based solution to predict Radio Link Failure (RLF). We combine Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) to find the correlation between radio measurements and RLF. The RLF prediction model was trained with real data obtained from a 5G testbed. In the second contribution, we propose a DRL-based solution to reduce UL latency. Our solution dynamically allocates the future UL grant by learning from the dynamic traffic pattern. In the last contribution, we introduce a DRL-based solution to balance latency and energy consumption by jointly deriving the C-DRX parameters and the BWP configuration
10
Chagdali, Abdellatif. "Multi-connectivity and resource allocation for slices in 5G networks." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPAST052.
Full textAbstract:
Les futurs réseaux mobiles promettent des opportunités sans précédent pour l'innovation et des cas d'utilisation disruptifs. L'engagement des réseaux 5G et au-delà à fournir des applications critiques nécessite un réseau polyvalent, évolutif, efficace et rentable, capable d'adapter son allocation de ressources pour répondre aux exigences de services hétérogènes. Pour relever ces défis, le découpage du réseau s'est imposé comme l'un des concepts fondamentaux proposés pour améliorer l'efficacité des réseaux mobiles 5G et leur conférer la plasticité requise. L'idée est de fournir des ressources à différentes industries verticales en construisant plusieurs réseaux logiques de bout en bout sur une infrastructure virtualisée partagée. Chaque "tranche de réseau" ainsi définie est personnalisée pour fournir un service spécifique en adaptant son architecture et ses technologies d'accès radio.Précisément, des applications telles que l'automatisation industrielle ou les communications entre véhicules imposent aux réseaux cellulaires des exigences strictes en matière de latence et de fiabilité. Étant donné que le réseau mobile actuel ne peut pas répondre à ces exigences, les communications ultra-fiables et à faible temps de latence constituent un sujet de recherche essentiel qui a suscité un élan considérable de la part du monde universitaire et des alliances industrielles. Pour répondre à ces exigences, l'utilisation de la multi-connectivité, c'est-à-dire l'exploitation simultanée de plusieurs liaisons radio comme voies de communication, est une approche prometteuse.L'objectif du présent manuscrit est d'étudier des techniques d'allocation de resources exploitant la couverture redondante des utilisateurs, garantie dans de nombreux scénarios 5G. Nous examinons d'abord l'évolution des réseaux mobiles et discutons des diverses considérations relatives à l'architecture de découpage du réseau et de son impact sur la conception des méthodes d'allocation des ressources. Nous utilisons ensuite les outils de la théorie des files d'attente pour modéliser un système dans lequel un ensemble d'utilisateurs URLLC sont connectés simultanément à deux stations de base ayant la même bande passante ; nous appelons ce scénario le cas homogène. Nous introduisons des politiques d'allocation appropriées et évaluons leurs performances respectives en évaluant leur fiabilité. Ensuite, nous étendons les résultats du cas homogène à un cadre plus général où les interfaces physiques gèrent des bandes passantes différentes, que nous appelons le cas hétérogène. Enfin, nous fusionnons les éléments ci-dessus pour valider le choix des schémas d'allocation des ressources en tenant compte de l'architecture déployée<br>Future mobile networks envision unprecedented innovation opportunities and disruptive use cases. As a matter of fact, the 5G and beyond networks' pledge to deliver mission-critical applications mandates a versatile, scalable, efficient, and cost-effective network capable of accommodating its resource allocation to meet the services' heterogeneous requirements. To face these challenges, network slicing has emerged as one of the fundamental concepts proposed to raise the 5G mobile networks' efficiency and provide the required plasticity. The idea is to provide resources for different vertical industries by building multiple end-to-end logical networks over a shared virtualized infrastructure. Each network slice is customized to deliver a specific service and adapts its architecture and radio access technologies.Precisely, applications such as industrial automation or vehicular communications pose stringent latency and reliability requirements on cellular networks. Given that the current mobile network cannot meet these requirements, ultra-reliable low-latency communications (URLLC) embodies a vital research topic that has gathered substantial momentum from academia and industrial alliances. To reach URLLC requirements, employing multi-connectivity (MC), i.e., exploiting multiple radio links as communication paths at once, is a promising approach.Therefore, the objective of the present manuscript is to investigate dynamic scheduling techniques, exploiting redundant coverage of users, guaranteed in numerous 5G radio access network scenarios. We first review the evolution of mobile networks and discuss various considerations for network slicing architecture and its impact on resource allocation design. Then, we use tools from queuing theory to model a system in which a set of URLLC users are connected simultaneously to two base stations having the same bandwidth; we refer to this scenario as the homogenous case. We introduce suitable scheduling policies and evaluate their respective performances by assessing their reliability. Next, we extend the homogenous case's results to a more general setting where the physical interfaces manage different bandwidths, referred to as the heterogeneous case. Finally, we merge the above elements to validate the choice of resource allocation schemes considering the deployed architecture
Books on the topic "Network slicing in 5G"
1
Kazmi, S. M. Ahsan, Latif U. Khan, Nguyen H. Tran, and Choong Seon Hong. Network Slicing for 5G and Beyond Networks. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16170-5.
Full text
2
Ye, Qiang, and Weihua Zhuang. Intelligent Resource Management for Network Slicing in 5G and Beyond. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88666-0.
Full text
3
Shetty, Rajaneesh Sudhakar. 5G Mobile Core Network. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6473-7.
Full text
4
Zhang, Ying. Network Function Virtualization: Concepts and Applicability in 5G Networks. John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119390633.
Full text
5
Du, Zhiyong, Bin Jiang, Qihui Wu, Yuhua Xu, and Kun Xu. Towards User-Centric Intelligent Network Selection in 5G Heterogeneous Wireless Networks. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1120-2.
Full text
6
Hong, Choong Seon, S. M. Ahsan Kazmi, Latif U. Khan, and Nguyen H. Tran. Network Slicing for 5G and Beyond Networks. Springer, 2020.
Find full text
7
Hong, Choong Seon, S. M. Ahsan Kazmi, Latif U. Khan, and Nguyen H. Tran. Network Slicing for 5G and Beyond Networks. Springer, 2019.
Find full text
8
Zhang, Lei, Arman Farhang, Gang Feng, and Oluwakayode Onireti, eds. Radio Access Network Slicing and Virtualization for 5G Vertical Industries. Wiley, 2020. http://dx.doi.org/10.1002/9781119652434.
Full text
9
Zhuang, Weihua, and Qiang Ye. Intelligent Resource Management for Network Slicing in 5G and Beyond. Springer International Publishing AG, 2022.
Find full text
10
Feng, Gang, Lei Zhang, Oluwakayode Onireti, and Arman Farhang. Radio Access Network Slicing and Virtualization for 5G Vertical Industries. Wiley & Sons, Limited, John, 2020.
Find full textBook chapters on the topic "Network slicing in 5G"
1
Kaloxylos, Alexandros, Christian Mannweiler, Gerd Zimmermann, et al. "Network Slicing." In 5G System Design. John Wiley & Sons, Ltd, 2018. http://dx.doi.org/10.1002/9781119425144.ch8.
Full text
2
Costa-Pérez, Xavier, Andrés Garcia-Saavedra, Fabio Giust, et al. "Network Slicing for 5G Networks." In 5G Networks: Fundamental Requirements, Enabling Technologies, and Operations Management. John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781119333142.ch9.
Full text
3
Kazmi, S. M. Ahsan, Latif U. Khan, Nguyen H. Tran, and Choong Seon Hong. "5G Networks." In Network Slicing for 5G and Beyond Networks. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16170-5_1.
Full text
4
Wu, Tin-Yu, and Tey Fu Jie. "5G Network Slicing Security." In Advances in Computing, Informatics, Networking and Cybersecurity. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87049-2_28.
Full text
5
Kazmi, S. M. Ahsan, Latif U. Khan, Nguyen H. Tran, and Choong Seon Hong. "Network Slicing: The Concept." In Network Slicing for 5G and Beyond Networks. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16170-5_2.
Full text
6
Kazmi, S. M. Ahsan, Latif U. Khan, Nguyen H. Tran, and Choong Seon Hong. "Resource Management for Network Slicing." In Network Slicing for 5G and Beyond Networks. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16170-5_3.
Full text
7
Kazmi, S. M. Ahsan, Latif U. Khan, Nguyen H. Tran, and Choong Seon Hong. "Network Slicing: Radio Resource Allocation." In Network Slicing for 5G and Beyond Networks. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16170-5_4.
Full text
8
Bakmaz, Bojan, and Miodrag Bakmaz. "5G Network Slicing: Principles, Architectures, and Challenges." In 5G Multimedia Communication. CRC Press, 2020. http://dx.doi.org/10.1201/9781003096450-8.
Full text
9
Ye, Qiang, and Wen Wu. "Network Slicing for 5G Networks and Beyond." In Wireless Networks. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98064-1_2.
Full text
10
Kazmi, S. M. Ahsan, Latif U. Khan, Nguyen H. Tran, and Choong Seon Hong. "Network Slicing: Cache and Backhaul Resource Allocation." In Network Slicing for 5G and Beyond Networks. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16170-5_6.
Full textConference papers on the topic "Network slicing in 5G"
1
Kaloxylos, Alexandros. "Network slicing for 5G networks." In PCI 2017: 21st PAN-HELLENIC CONFERENCE ON INFORMATICS. ACM, 2017. http://dx.doi.org/10.1145/3139367.3139392.
Full text
2
Jácome, William F. Villota, Oscar M. Caicedo Rendon, and Nelson L. S. da Fonseca. "Admission Control and Resource Allocation in 5G Network Slicing." In Anais Estendidos do Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/sbrc_estendido.2021.17158.
Full textAbstract:
This paper summarizes the research in the master thesis entitled "Admission Control and Resource Allocation in 5G Network Slicing". We propose two solutions, SARA and DSARA, based on Reinforcement Learning algorithms to learn the admission policy that optimizes the profit of providers. Resource allocation considers the QoS requirements. Results show the outstanding performance of our solutions to 5G Network Slicing in relation to profit and resource utilization.
3
Yoo, Taewhan. "Network slicing architecture for 5G network." In 2016 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2016. http://dx.doi.org/10.1109/ictc.2016.7763354.
Full text
4
Kourtis, Michail-Alexandros, Themis Anagnostopoulos, Slawomir Kuklilski, et al. "5G Network Slicing Enabling Edge Services." In 2020 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN). IEEE, 2020. http://dx.doi.org/10.1109/nfv-sdn50289.2020.9289880.
Full text
5
Alexander, Henry, Hesham El-Sayed, Manzoor Ahmed Khan, and Parag Kulkarni. "Flexibly Controlled 5G Network Slicing." In 2022 5th International Conference on Communications, Signal Processing, and their Applications (ICCSPA). IEEE, 2022. http://dx.doi.org/10.1109/iccspa55860.2022.10019226.
Full text
6
Sevim, Kubra, and Tuna Tugcu. "Handover with Network Slicing in 5G Networks." In 2021 International Conference on Computer, Information and Telecommunication Systems (CITS). IEEE, 2021. http://dx.doi.org/10.1109/cits52676.2021.9618576.
Full text
7
Alberti, Antonio Marcos, Karine Costa, and Tibério Tavares Tavares Rezende. "Virtualização em Redes Terrestre-Satélite 5G." In I Workshop de Teoria, Tecnologias e Aplicações de Slicing para Infraestruturas Softwarizadas. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/wslice.2019.7723.
Full textAbstract:
In order to propose a hybrid terrestrial-satellite architecture for the 5G infrastructure in Brazil, it is sought to discover and understand the resources and tools used in the simulation or emulation of these network architectures for performance analysis purposes when evaluating various technological interoperability scenarios. Based on the main projects of the European Commission H2020 and standardization organizations of the 5G network, such as 3GPP, ETSI, ITU, among others, this article investigates how the standardization and implementation of 5G terrestrial network management and virtualization technologies are being applied. It highlights the use cases and the tools used in their investigations.
8
Noll, Josef, Sudhir Dixit, Danica Radovanovic, Maghsoud Morshedi, Christine Holst, and Andrea S. Winkler. "5G network slicing for digital inclusion." In 2018 10th International Conference on Communication Systems & Networks (COMSNETS). IEEE, 2018. http://dx.doi.org/10.1109/comsnets.2018.8328197.
Full text
9
Chatras, Bruno, U. Steve Tsang Kwong, and Nicolas Bihannic. "NFV enabling network slicing for 5G." In 2017 20th Conference on Innovations in Clouds, Internet and Networks (ICIN). IEEE, 2017. http://dx.doi.org/10.1109/icin.2017.7899415.
Full text
10
Wen, Ruihan, Gang Feng, Jianhong Zhou, and Shuang Qin. "Mobility Management for Network Slicing Based 5G Networks." In 2018 IEEE 18th International Conference on Communication Technology (ICCT). IEEE, 2018. http://dx.doi.org/10.1109/icct.2018.8600026.
Full textReports on the topic "Network slicing in 5G"
1
Goreczky, Péter. 5G Network Rollout: a Contest of Countries or Companies? Külügyi és Külgazdasági Intézet, 2021. http://dx.doi.org/10.47683/kkielemzesek.e-2021.14.
Full textAbstract:
In terms of the number of 5G subscriptions, Northeast Asia, including China, is currently ahead of the rest of the world. The global 5G infrastructure market is expected to continue to grow rapidly over the next five years, but the elite club of global companies capable of building it is not expected to expand in the short term. Despite restrictions on Huawei’s participation in some tenders and possible Chinese retaliation against European companies, neither the Chinese company nor Ericsson is expected to be sidelined from the global market for 5G network infrastructure devices. The new networks are likely to be future drivers of economic growth, and the ‘5G competition’ of countries aims to create the necessary technological foundations. Western sanctions against Chinese technology companies will not hold back the construction of 5G and, through it, the future development of the Chinese economy. In addition to establishing a network, countries need companies that really transform their activities by building on new technologies. In the absence of these, 5G rollout can easily remain an unfulfilled promise for economic growth.
2
Housley, R., S. Turner, J. Preuß Mattsson, and D. Migault. X.509 Certificate Extension for 5G Network Function Types. RFC Editor, 2023. http://dx.doi.org/10.17487/rfc9310.
Full text
3
Reddy.K, T., J. Ekman, and D. Migault. X.509 Certificate Extended Key Usage (EKU) for 5G Network Functions. RFC Editor, 2024. http://dx.doi.org/10.17487/rfc9509.
Full text
4
Wendt-Lucas, Nicola, and Ana de Jesus. The Role of 5G in the Transition to a Digital and Green Economy in the Nordic and Baltic Countries: Analytic Report. Nordregio, 2023. http://dx.doi.org/10.6027/r2023:7.1403-2503.
Full textAbstract:
The report provides an overview of the status of 5G roll-out and its industrial uptake in the Nordic-Baltic region. The aim is not only to present the roll-out status across the region but also to put these insights into a broader political and technological context. The report describes some of the most relevant testbeds and policy initiatives, provides examples of successful and promising use cases, and highlights existing strengths and ongoing challenges in the Nordic-Baltic region. Juxtaposing these against established goals emphasises areas for possible future cooperation and knowledge exchange between the Nordic and Baltic actors to encourage sustainable innovation and competitiveness across the region. The report reveals notable variations in the progress of 5G coverage among the Nordic-Baltic countries. Denmark and Finland have made significant strides and surpassed the European Union average regarding 5G coverage, while other countries still face challenges in catching up. Despite the presence of numerous 5G activities, such as testbeds and innovation hubs, a considerable number of initiatives remain in the testing and prototyping stage. The report indicates that the full potential of 5G for innovation and competitiveness in the Nordic-Baltic region has yet to be fully realised. With the accelerated digitalisation brought by 5G, the importance of cyber security considerations increases. As 5G networks increase electricity demand, it is essential to consider the environmental footprint and societal effects of their rollout. While 5G has immense potential, it necessitates significant adjustments in various areas, including network infrastructure, systems, applications, data ethics, privacy, and workforce implications. Challenges related to social inclusion further underscore the importance of cooperation and synergy across the region. The report highlights the need for increased knowledge exchange, the formulation of common roadmaps, and the establishment of guidelines to promote harmonised 5G deployment. Collaborative efforts among stakeholders are crucial for maximising the benefits of 5G technology in the Nordic and Baltic countries.
5
Al-Qadi, Imad, Yanfeng Ouyang, Eleftheria Kontou, et al. Planning for Emerging Mobility: Testing and Deployment in Illinois. Illinois Center for Transportation, 2023. http://dx.doi.org/10.36501/0197-9191/23-025.
Full textAbstract:
As part of planning I-ACT, this project developed plans and recommendations for the integration of energy harvesting, electrification, and 5G communication. Given the emergence of autonomous, connected, and electrified technologies, I-ACT will serve a critical role for the state of Illinois in developing, testing, and validating mobility technologies, along with establishing an integrated infrastructure that will safely and reliably serve all users. A summary of recommendations related to the design features of I-ACT is presented for energy harvesting, electrification, and 5G communication network. Moreover, a business plan was developed. All findings and recommendations were provided to AECOM, as part of the I-ACT’s conceptualization phase.
6
Szabó, Bianka Emese. China’s Increasing Presence in the ICT Sector in Serbia. Külügyi és Külgazdasági Intézet, 2021. http://dx.doi.org/10.47683/kkielemzesek.ke-2021.19.
Full textAbstract:
Along with the Serbian government’s statements emphasizing the importance of digitalisation and the need for innovation, China is starting to penetrate sensitive domains in Serbia in terms of national security. This intensifying cooperation also extends to critical assets in the ICT sector, such as digital education, e-governance, biometric mass surveillance, and the 5G network, raising concerns from Western powers. The aim of this analysis is to provide an overview of the projects involved in the cooperation and reflect on the sensitive issues that could lead to disagreements between Serbia and its great power allies in the future.
7
García Zaballos, Antonio, Maribel Dalio, Jesús Garran, Enrique Iglesias Rodriguez, Pau Puig Gabarró, and Ricardo Martínez Garza Fernández. Estructuración de un centro de operación de redes (NOC). Banco Interamericano de Desarrollo, 2022. http://dx.doi.org/10.18235/0004520.
Full textAbstract:
El desarrollo de la tecnología 5G nos permitirá descubrir cosas que hasta ahora no podíamos hacer tecnológicamente. Y habilitará la fusión del mundo físico (operational technology, OT) con el mundo digital (information technology, IT), abriendo nuevos mundos como son internet de las cosas (Internet of Things, IoT), metaverso, digital twin, entre otros. El centro de operaciones de red (Network Operations Center, NOC) es la herramienta que nos permite garantizar la disponibilidad y el rendimiento de las redes. En particular, el NOC será responsable de monitorizar, identificar, investigar, priorizar, resolver o escalar incidentes en la red, que pueden afectar o que están afectando su disponibilidad o rendimiento. Esta publicación presenta y desarrolla la hoja de ruta para diseñar y poner en marcha un NOC que contribuya al éxito de la implementación de la estrategia nacional de conectividad y garantice el funcionamiento correcto de la infraestructura digital, facilitando la conectividad de comunidades e instituciones.