1,969 research outputs found
Creating Tailored and Adaptive Network Services with the Open Orchestration C-RAN Framework
Next generation wireless communications networks will leverage
software-defined radio and networking technologies, combined with cloud and fog
computing. A pool of resources can then be dynamically allocated to create
personalized network services (NSs). The enabling technologies are abstraction,
virtualization and consolidation of resources, automatization of processes, and
programmatic provisioning and orchestration. ETSI's network functions
virtualization (NFV) management and orchestration (MANO) framework provides the
architecture and specifications of the management layers. We introduce OOCRAN,
an open-source software framework and testbed that extends existing NFV
management solutions by incorporating the radio communications layers. This
paper presents OOCRAN and illustrates how it monitors and manages the pool of
resources for creating tailored NSs. OOCRAN can automate NS reconfiguration,
but also facilitates user control. We demonstrate the dynamic deployment of
cellular NSs and discuss the challenges of dynamically creating and managing
tailored NSs on shared infrastructure.Comment: IEEE 5G World Forum 201
AI-native Interconnect Framework for Integration of Large Language Model Technologies in 6G Systems
The evolution towards 6G architecture promises a transformative shift in
communication networks, with artificial intelligence (AI) playing a pivotal
role. This paper delves deep into the seamless integration of Large Language
Models (LLMs) and Generalized Pretrained Transformers (GPT) within 6G systems.
Their ability to grasp intent, strategize, and execute intricate commands will
be pivotal in redefining network functionalities and interactions. Central to
this is the AI Interconnect framework, intricately woven to facilitate
AI-centric operations within the network. Building on the continuously evolving
current state-of-the-art, we present a new architectural perspective for the
upcoming generation of mobile networks. Here, LLMs and GPTs will
collaboratively take center stage alongside traditional pre-generative AI and
machine learning (ML) algorithms. This union promises a novel confluence of the
old and new, melding tried-and-tested methods with transformative AI
technologies. Along with providing a conceptual overview of this evolution, we
delve into the nuances of practical applications arising from such an
integration. Through this paper, we envisage a symbiotic integration where AI
becomes the cornerstone of the next-generation communication paradigm, offering
insights into the structural and functional facets of an AI-native 6G network
Network slicing with flexible mobility and QoS/QoE support for 5G networks
Proceeding of: 2017 IEEE International Conference on Communications. Workshops (ICC Workshops)Network slicing is an emerging area of research, featuring a logical arrangement of resources to operate as individual networks, thus allowing for massively customizable service and tenant requirements. The focus of this paper is to present the design of a flexible 5G architecture for network slicing, building on SDN and NFV technologies as enablers. More specifically, we place the emphasis on techniques that provide efficient utilization of substrate resources for network slicing, ultimately optimizing network performance. The key areas of consideration in our architecture revolve around flexible service-tailored mobility, service-aware QoS/QoE control as well as network-wide orchestrationThis research work has been performed in the framework of H2020-ICT-2014-2 project 5G NORMA
Identifying 5G system enhancements: enabling technologies for multi-service networks
Proceeding of: 2018 IEEE Conference on Standards for Communications and Networking (CSCN)The fifth generation (5G) of mobile and wireless communications networks aims at addressing a diverse set of use cases, services, and applications with a particular focus on enabling new business cases via network slicing. The development of 5G has thus advanced quickly with research projects and standardization efforts resulting in the 5G baseline architecture. Nevertheless, for the realization of native end-to-end (E2E) network slicing, further features and optimizations shall still be introduced. In this paper, we provide a gap analysis of current 5G system (5GS) with respect to some specific enhancements and detail our insights on the enabling innovations that can fill the identified gaps. We will then discuss the essential building blocks and design principles of an evolved 5G baseline architecture capitalizing on the innovations that are being developed.This work has been performed in the framework of the H2020 project 5G-MoNArch co-funded by the EU
View on 5G Architecture: Version 1.0
The current white paper focuses on the produced results after one year research mainly from 16 projects working on the abovementioned domains. During several months, representatives from these projects have worked together to identify the key findings of their projects and capture the commonalities and also the different approaches and trends. Also they have worked to determine the challenges that remain to be overcome so as to meet the 5G requirements. The goal of 5G Architecture Working Group is to use the results captured in this white paper to assist the participating projects achieve a common reference framework. The work of this working group will continue during the following year so as to capture the latest results to be produced by the projects and further elaborate this reference framework. The 5G networks will be built around people and things and will natively meet the requirements of three groups of use cases: • Massive broadband (xMBB) that delivers gigabytes of bandwidth on demand • Massive machine-type communication (mMTC) that connects billions of sensors and machines • Critical machine-type communication (uMTC) that allows immediate feedback with high reliability and enables for example remote control over robots and autonomous driving. The demand for mobile broadband will continue to increase in the next years, largely driven by the need to deliver ultra-high definition video. However, 5G networks will also be the platform enabling growth in many industries, ranging from the IT industry to the automotive, manufacturing industries entertainment, etc. 5G will enable new applications like for example autonomous driving, remote control of robots and tactile applications, but these also bring a lot of challenges to the network. Some of these are related to provide low latency in the order of few milliseconds and high reliability compared to fixed lines. But the biggest challenge for 5G networks will be that the services to cater for a diverse set of services and their requirements. To achieve this, the goal for 5G networks will be to improve the flexibility in the architecture. The white paper is organized as follows. In section 2 we discuss the key business and technical requirements that drive the evolution of 4G networks into the 5G. In section 3 we provide the key points of the overall 5G architecture where as in section 4 we elaborate on the functional architecture. Different issues related to the physical deployment in the access, metro and core networks of the 5G network are discussed in section 5 while in section 6 we present software network enablers that are expected to play a significant role in the future networks. Section 7 presents potential impacts on standardization and section 8 concludes the white paper
A unified service-based capability exposure framework for closed-loop network automation
The ongoing quest for the tight integration of network operation and the network service provisioning initiated with the introduction of 5G often clashes with the capacity of current network architectures to provide means for such integration. Owing to the traditional design of mobile networks, which barely required a tight interaction, network elements offer capabilities for their continuous optimization just within their domain (eg, access, or core), allowing for a "silo-style" automation that falls short when aiming at closed-loop automation that embraces all the actors involved in the network, from network functions up to the service-provider network functions. To this end, in this article, we make the case for the network-wide capability exposure framework for closed-loop automation by (i) defining the different entities that shall expose capabilities, and (ii) discussing why the state of the art solutions are not enough to support this vision. Our proposed architecture, which relies on registration and discovery, and exposure functions, allows for enhanced use cases that are currently not possible with state of the art solution. We prove the feasibility of our solution by implementing it in a real-world testbed, employing Artificial Intelligence algorithms to close the loop for the management of the radio access network.Part of this work was performed in the context of the H2020 5G-MoNArch project (grant agreement no. 761445). The work of Marco Gramaglia has been partially funded by the H2020 5G-TOURS project (grant agreement no. 856950), and by the Spanish Ministry of Economic Affairs and Digital Transformation and the European Union-NextGenerationEU through the UNICO 5G I+D projects 6G-CLARION-NFD, 6G-CLARION-OR, 6G-CLARION-SI, and 6G-CLARION-O
Software-Defined Networks for Future Networks and Services: Main Technical Challenges and Business Implications
In 2013, the IEEE Future Directions Committee (FDC) formed an SDN work group to explore the amount of interest in forming an IEEE Software-Defined Network (SDN) Community. To this end, a Workshop on "SDN for Future Networks and Services" (SDN4FNS'13) was organized in Trento, Italy (Nov. 11th-13th 2013). Following the results of the workshop, in this paper, we have further analyzed scenarios, prior-art, state of standardization, and further discussed the main technical challenges and socio-economic aspects of SDN and virtualization in future networks and services. A number of research and development directions have been identified in this white paper, along with a comprehensive analysis of the technical feasibility and business availability of those fundamental technologies. A radical industry transition towards the "economy of information through softwarization" is expected in the near future
On the Rollout of Network Slicing in Carrier Networks: A Technology Radar
Network slicing is a powerful paradigm for network operators to support use cases with
widely diverse requirements atop a common infrastructure. As 5G standards are completed, and
commercial solutions mature, operators need to start thinking about how to integrate network slicing
capabilities in their assets, so that customer-facing solutions can be made available in their portfolio.
This integration is, however, not an easy task, due to the heterogeneity of assets that typically exist
in carrier networks. In this regard, 5G commercial networks may consist of a number of domains,
each with a different technological pace, and built out of products from multiple vendors, including
legacy network devices and functions. These multi-technology, multi-vendor and brownfield features
constitute a challenge for the operator, which is required to deploy and operate slices across all these
domains in order to satisfy the end-to-end nature of the services hosted by these slices. In this context,
the only realistic option for operators is to introduce slicing capabilities progressively, following a
phased approach in their roll-out. The purpose of this paper is to precisely help designing this kind
of plan, by means of a technology radar. The radar identifies a set of solutions enabling network
slicing on the individual domains, and classifies these solutions into four rings, each corresponding
to a different timeline: (i) as-is ring, covering today’s slicing solutions; (ii) deploy ring, corresponding
to solutions available in the short term; (iii) test ring, considering medium-term solutions; and
(iv) explore ring, with solutions expected in the long run. This classification is done based on the
technical availability of the solutions, together with the foreseen market demands. The value of this
radar lies in its ability to provide a complete view of the slicing landscape with one single snapshot,
by linking solutions to information that operators may use for decision making in their individual
go-to-market strategies.H2020 European Projects 5G-VINNI (grant agreement No. 815279) and 5G-CLARITY (grant agreement No. 871428)Spanish national project TRUE-5G (PID2019-108713RB-C53
Recent Advances in Machine Learning for Network Automation in the O-RAN
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/The evolution of network technologies has witnessed a paradigm shift toward open and intelligent networks, with the Open Radio Access Network (O-RAN) architecture emerging as a promising solution. O-RAN introduces disaggregation and virtualization, enabling network operators to deploy multi-vendor and interoperable solutions. However, managing and automating the complex O-RAN ecosystem presents numerous challenges. To address this, machine learning (ML) techniques have gained considerable attention in recent years, offering promising avenues for network automation in O-RAN. This paper presents a comprehensive survey of the current research efforts on network automation using ML in O-RAN. We begin by providing an overview of the O-RAN architecture and its key components, highlighting the need for automation. Subsequently, we delve into O-RAN support for ML techniques. The survey then explores challenges in network automation using ML within the O-RAN environment, followed by the existing research studies discussing application of ML algorithms and frameworks for network automation in O-RAN. The survey further discusses the research opportunities by identifying important aspects where ML techniques can benefit.Peer reviewe
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