AI-Native Multi-Access Future Networks -- The REASON Architecture
- URL: http://arxiv.org/abs/2411.06870v2
- Date: Mon, 25 Nov 2024 11:58:44 GMT
- Title: AI-Native Multi-Access Future Networks -- The REASON Architecture
- Authors: Konstantinos Katsaros, Ioannis Mavromatis, Kostantinos Antonakoglou, Saptarshi Ghosh, Dritan Kaleshi, Toktam Mahmoodi, Hamid Asgari, Anastasios Karousos, Iman Tavakkolnia, Hossein Safi, Harald Hass, Constantinos Vrontos, Amin Emami, Juan Parra Ullauri, Shadi Moazzeni, Dimitra Simeonidou,
- Abstract summary: REASON project aims to address technical challenges in future network deployments, such as E2E service orchestration, sustainability, security and trust management.
This paper presents REASON's architecture and the identified requirements for future networks.
- Score: 4.323505243954935
- License:
- Abstract: The development of the sixth generation of communication networks (6G) has been gaining momentum over the past years, with a target of being introduced by 2030. Several initiatives worldwide are developing innovative solutions and setting the direction for the key features of these networks. Some common emerging themes are the tight integration of AI, the convergence of multiple access technologies and sustainable operation, aiming to meet stringent performance and societal requirements. To that end, we are introducing REASON - Realising Enabling Architectures and Solutions for Open Networks. The REASON project aims to address technical challenges in future network deployments, such as E2E service orchestration, sustainability, security and trust management, and policy management, utilising AI-native principles, considering multiple access technologies and cloud-native solutions. This paper presents REASON's architecture and the identified requirements for future networks. The architecture is meticulously designed for modularity, interoperability, scalability, simplified troubleshooting, flexibility, and enhanced security, taking into consideration current and future standardisation efforts, and the ease of implementation and training. It is structured into four horizontal layers: Physical Infrastructure, Network Service, Knowledge, and End-User Application, complemented by two vertical layers: Management and Orchestration, and E2E Security. This layered approach ensures a robust, adaptable framework to support the diverse and evolving requirements of 6G networks, fostering innovation and facilitating seamless integration of advanced technologies.
Related papers
- Overview of AI and Communication for 6G Network: Fundamentals, Challenges, and Future Research Opportunities [148.601430677814]
This paper presents a comprehensive overview of AI and communication for 6G networks.
We first review the driving factors behind incorporating AI into wireless communications, as well as the vision for the convergence of AI and 6G.
The discourse then transitions to a detailed exposition of the envisioned integration of AI within 6G networks.
arXiv Detail & Related papers (2024-12-19T05:36:34Z) - Distributed satellite information networks: Architecture, enabling technologies, and trends [56.747473208256174]
The distributed satellite information networks (DSIN) have emerged as an innovative architecture, bridging information gaps across diverse satellite systems.
This survey first provides a profound discussion about innovative network architectures of DSIN.
The DSIN faces challenges from network heterogeneity, unpredictable channel dynamics, sparse resources, and decentralized collaboration frameworks.
arXiv Detail & Related papers (2024-12-17T06:44:05Z) - Transforming the Hybrid Cloud for Emerging AI Workloads [81.15269563290326]
This white paper envisions transforming hybrid cloud systems to meet the growing complexity of AI workloads.
The proposed framework addresses critical challenges in energy efficiency, performance, and cost-effectiveness.
This joint initiative aims to establish hybrid clouds as secure, efficient, and sustainable platforms.
arXiv Detail & Related papers (2024-11-20T11:57:43Z) - Large Language Models meet Network Slicing Management and Orchestration [0.3644165342767221]
This paper proposes a future vision for network slicing powered by Large Language Models (LLMs) and multi-agent systems.
We discuss the challenges associated with implementing this framework and potential solutions to mitigate them.
arXiv Detail & Related papers (2024-03-20T16:29:52Z) - Enhancing Network Slicing Architectures with Machine Learning, Security,
Sustainability and Experimental Networks Integration [0.21200026734831154]
Network Slicing (NS) is an essential technique extensively used in 5G networks computing strategies.
NS is foreseen as one of the leading enablers for 6G futuristic and highly demanding applications.
NS architecture proposals typically fulfill the needs of specific sets of domains with commonalities.
arXiv Detail & Related papers (2023-07-18T11:22:31Z) - Holistic Network Virtualization and Pervasive Network Intelligence for
6G [14.35331138476144]
We look into the evolution and prospect of network architecture and propose a novel conceptual architecture for the 6th generation (6G) networks.
The proposed architecture has two key elements, i.e., holistic network virtualization and pervasive artificial intelligence (AI)
We aim to inspire further discussions and developments on the potential architecture of 6G.
arXiv Detail & Related papers (2023-01-02T04:15:33Z) - Transformer-Empowered 6G Intelligent Networks: From Massive MIMO
Processing to Semantic Communication [71.21459460829409]
We introduce an emerging deep learning architecture, known as the transformer, and discuss its potential impact on 6G network design.
Specifically, we propose transformer-based solutions for massive multiple-input multiple-output (MIMO) systems and various semantic communication problems in 6G networks.
arXiv Detail & Related papers (2022-05-08T03:22:20Z) - Introduction to the Artificial Intelligence that can be applied to the
Network Automation Journey [68.8204255655161]
The "Intent-Based Networking - Concepts and Definitions" document describes the different parts of the ecosystem that could be involved in NetDevOps.
The recognize, generate intent, translate and refine features need a new way to implement algorithms.
arXiv Detail & Related papers (2022-04-02T08:12:08Z) - Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and
Applications [39.223546118441476]
6G will revolutionize the evolution of wireless from "connected things" to "connected intelligence"
Deep learning and big data analytics based AI systems require tremendous computation and communication resources.
edge AI stands out as a disruptive technology for 6G to seamlessly integrate sensing, communication, computation, and intelligence.
arXiv Detail & Related papers (2021-11-24T11:47:16Z) - Intelligent Zero Trust Architecture for 5G/6G Tactical Networks:
Principles, Challenges, and the Role of Machine Learning [4.314956204483074]
We highlight the challenges and introduce the concept of an intelligent zero trust architecture (i-ZTA) as a security framework in 5G/6G networks with untrusted components.
This paper presents the architectural design of an i-ZTA upon which modern artificial intelligence (AI) algorithms can be developed to provide information security in untrusted networks.
arXiv Detail & Related papers (2021-05-04T13:14:29Z) - Towards Self-learning Edge Intelligence in 6G [143.1821636135413]
Edge intelligence, also called edge-native artificial intelligence (AI), is an emerging technological framework focusing on seamless integration of AI, communication networks, and mobile edge computing.
In this article, we identify the key requirements and challenges of edge-native AI in 6G.
arXiv Detail & Related papers (2020-10-01T02:16:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.